Computational biology / bioinformatics Books

Book SynopsisOne of the world’s leading cultural psychologists debunks the hype surrounding DNA testing and puts to rest our mistaken anxieties about our genes.

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Book SynopsisBioinformatics is an extremely popular and rapidly growing new discipline that has evolved around the use of algorithmic and computer techniques to analyze large datasets being generated in genomics and related fields. Bioinformatics: Genomics and Post-Genomics provides a clear and concise introduction to the popular new science of bioinformatics.Trade Review"...provides a clear and concise introduction to the popular new science of bioinformatics." (Bioautomation, volume 7)Table of ContentsChapter 1. Genome sequencing. 1.1 Automatic sequencing. 1.2 Sequencing strategies. 1.3 Fragmentation strategies. 1.4 Sequence assembly. 1.5 Filling gaps. 1.6 Obstacles to reconstruction. 1.7 Utilizing a complementary ‘large’ clone library. 1.8 The first large-scale sequencing project: The Haemophilus influenzae genome. 1.9 cDNA and EST. Chapter 2. Sequence comparisons. 2.1 Introduction: Comparison as a sequence prediction method. 2.2 A sample molecule: the human and rosterone receptor. 2.3 Sequence homologies - functional homologies. 2.4 Comparison matrices. 2.5 The problem of insertions and deletions. 2.6 Optimal alignment: the dynamic programming method. 2.7 Fast heuristic methods. 2.8 Sensitivity, specificity, and confidence level. 2.9 Multiple alignments. Chapter 3. Comparative genomics. 3.1 General properties of genomes. 3.2 Genome comparisons. 3.3 Gene evolution and phylogeny: applications to annotation. Chapter 4. Genetic information and biological sequences. 4.1 Introduction: Coding levels. 4.2 Genes and the genetic code. 4.3 Expression signals. 4.4 Specific sites. 4.5 Sites located on DNA. 4.6 Sites present on RNA. 4.7 Pattern detection methods. Chapter 5. Statistics and sequences. 5.1 Introduction. 5.2 Nucleotide base and amino acid distribution. 5.3 The biological basis of codon bias. 5.4 Using statistical bias for prediction. 5.5 Modeling DNA sequences. 5.6 Complex models. 5.7 Sequencing errors and hidden Markov models. 5.8 Hidden Markov processes: a general sequence analysis tool. 5.9 The search for genes - a difficult art. Chapter 6. Structure prediction. 6.1 The structure of RNA. 6.2 Properties of the RNA molecule. 6.3 Secondary RNA structures. 6.4 Thermodynamic stability of RNA structures. 6.5 Finding the most stable structure. 6.6 Validation of predicted secondary structures. 6.7 Using chemical and enzymatic probing to analyze folding. 6.8 Long-distance interactions and three-dimensional structure prediction. 6.9 Protein structure. 6.10 Secondary structure prediction. 6.11 Three-dimensional modeling based on homologous protein structure. 6.12 Predicting folding. Chapter 7. Transcriptome and proteome: macromolecular networks. 7.1 Introduction. 7.2 Post-genomic methods. 7.3 Macromolecular networks. 7.4 Topology of macromolecular networks. 7.5 Modularity and dynamics of macromolecular networks. 7.6 Inference of regulatory networks. Chapter 8. Simulation of Biological Processes in the Genome Context. 8.1 Types of simulations. 8.2 Prediction and explanation. 8.3 Simulation of molecular networks. 8.4 Generic post-genomic simulators. Index.

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Book SynopsisData Analysis and Visualization in Genomics and Proteomics is the first book addressing integrative data analysis and visualization in this field. It addresses important techniques for the interpretation of data originating from multiple sources, encoded in different formats or protocols, and processed by multiple systems.Table of ContentsPreface. List of Contributors. SECTION I: INTRODUCTION - DATA DIVERSITY AND INTEGRATION. 1. Integrative Data Analysis and Visualization: Introduction to Critical Problems, Goals and Challenges (Francisco Azuaje and Joaquín Dopazo). 1.1 Data Analysis and Visualization: An Integrative Approach. 1.2 Critical Design and Implementation Factors. 1.3 Overview of Contributions. References. 2. Biological Databases: Infrastructure, Content and Integration (Allyson L. Williams, Paul J. Kersey, Manuela Pruess and Rolf Apweiler). 2.1 Introduction. 2.2 Data Integration. 2.3 Review of Molecular Biology Databases. 2.4 Conclusion. References. 3. Data and Predictive Model Integration: an Overview of Key Concepts, Problems and Solutions (Francisco Azuaje, Joaquín Dopazo and Haiying Wang). 3.1 Integrative Data Analysis and Visualization: Motivation and Approaches. 3.2 Integrating Informational Views and Complexity for Understanding Function. 3.3 Integrating Data Analysis Techniques for Supporting Functional Analysis. 3.4 Final Remarks. References. SECTION II: INTEGRATIVE DATA MINING AND VISUALIZATION -EMPHASIS ON COMBINATION OF MULTIPLE DATA TYPES. 4. Applications of Text Mining in Molecular Biology, from Name Recognition to Protein Interaction Maps (Martin Krallinger and Alfonso Valencia). 4.1 Introduction. 4.2 Introduction to Text Mining and NLP. 4.3 Databases and Resources for Biomedical Text Mining. 4.4 Text Mining and Protein-Protein Interactions. 4.5 Other Text-Mining Applications in Genomics. 4.6 The Future of NLP in Biomedicine. Acknowledgements. References. 5. Protein Interaction Prediction by Integrating Genomic Features and Protein Interaction Network Analysis (Long J. Lu, Yu Xia, Haiyuan Yu, Alexander Rives, Haoxin Lu, Falk Schubert and Mark Gerstein). 5.1 Introduction. 5.2 Genomic Features in Protein Interaction Predictions. 5.3 Machine Learning on Protein-Protein Interactions. 5.4 The Missing Value Problem. 5.5 Network Analysis of Protein Interactions. 5.6 Discussion. References. 6. Integration of Genomic and Phenotypic Data (Amanda Clare). 6.1 Phenotype. 6.2 Forward Genetics and QTL Analysis. 6.3 Reverse Genetics. 6.4 Prediction of Phenotype from Other Sources of Data. 6.5 Integrating Phenotype Data with Systems Biology. 6.6 Integration of Phenotype Data in Databases. 6.7 Conclusions. References. 7. Ontologies and Functional Genomics (Fátima Al-Shahrour and Joaquín Dopazo). 7.1 Information Mining in Genome-Wide Functional Analysis. 7.2 Sources of Information: Free Text Versus Curated Repositories. 7.3 Bio-Ontologies and the Gene Ontology in Functional Genomics. 7.4 Using GO to Translate the Results of Functional Genomic Experiments into Biological Knowledge. 7.5 Statistical Approaches to Test Significant Biological Differences. 7.6 Using FatiGO to Find Significant Functional Associations in Clusters of Genes. 7.7 Other Tools. 7.8 Examples of Functional Analysis of Clusters of Genes. 7.9 Future Prospects. References. 8. The C. elegans Interactome: its Generation and Visualization (Alban Chesnau and Claude Sardet). 8.1 Introduction. 8.2 The ORFeome: the first step toward the interactome of C. elegans. 8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans Protein-Protein Interaction (Interactome) Network: Technical Aspects. 8.4 Visualization and Topology of Protein-Protein Interaction Networks. 8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale Genomics and Post-Genomics Data Sets. 8.6 Conclusion: From Interactions to Therapies. References. SECTION III: INTEGRATIVE DATA MINING AND VISUALIZATION - EMPHASIS ON COMBINATION OF MULTIPLE PREDICTION MODELS AND METHODS. 9. Integrated Approaches for Bioinformatic Data Analysis and Visualization - Challenges, Opportunities and New Solutions (Steve R. Pettifer, James R. Sinnott and Teresa K. Attwood). 9.1 Introduction. 9.2 Sequence Analysis Methods and Databases. 9.3 A View Through a Portal. 9.4 Problems with Monolithic Approaches: One Size Does Not Fit All. 9.5 A Toolkit View. 9.6 Challenges and Opportunities. 9.7 Extending the Desktop Metaphor. 9.8 Conclusions. Acknowledgements. References. 10. Advances in Cluster Analysis of Microarray Data (Qizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal and Bart De Moor). 10.1 Introduction. 10.2 Some Preliminaries. 10.3 Hierarchical Clustering. 10.4 k-Means Clustering. 10.5 Self-Organizing Maps. 10.6 A Wish List for Clustering Algorithms. 10.7 The Self-Organizing Tree Algorithm. 10.8 Quality-Based Clustering Algorithms. 10.9 Mixture Models. 10.10 Biclustering Algorithms. 10.11 Assessing Cluster Quality. 10.12 Open Horizons. References. 11. Unsupervised Machine Learning to Support Functional Characterization of Genes: Emphasis on Cluster Description and Class Discovery (Olga G. Troyanskaya). 11.1 Functional Genomics: Goals and Data Sources. 11.2 Functional Annotation by Unsupervised Analysis of Gene Expression Microarray Data. 11.3 Integration of Diverse Functional Data For Accurate Gene Function Prediction. 11.4 MAGIC - General Probabilistic Integration of Diverse Genomic Data. 11.5 Conclusion. References. 12. Supervised Methods with Genomic Data: a Review and Cautionary View (Ramón Díaz-Uriarte). 12.1 Chapter Objectives. 12.2 Class Prediction and Class Comparison. 12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes. 12.4 Class Prediction and Prognostic Prediction. 12.5 ROC Curves for Evaluating Predictors and Differential Expression. 12.6 Caveats and Admonitions. 12.7 Final Note: Source Code Should be Available. Acknowledgements. References. 13. A Guide to the Literature on Inferring Genetic Networks by Probabilistic Graphical Models (Pedro Larrañaga, Iñaki Inza and Jose L. Flores). 13.1 Introduction. 13.2 Genetic Networks. 13.3 Probabilistic Graphical Models. 13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models. 13.5 Conclusions. Acknowledgements. References. 14. Integrative Models for the Prediction and Understanding of Protein Structure Patterns (Inge Jonassen). 14.1 Introduction. 14.2 Structure Prediction. 14.3 Classifications of Structures. 14.4 Comparing Protein Structures 14.5 Methods for the Discovery of Structure Motifs. 14.6 Discussion and Conclusions. References. Index.

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Book SynopsisThis book provides an introduction to both Bayesian methods and gene expression, accessible to people with backgrounds in either. The text is enhanced by the inclusion of numerous problems and solutions, designed with an emphasis on methodology and application.Trade Review“The target audience for this book is clearly statisticians rather than biologists … It does provide a very useful overview of statistical genomics for anyone working in the field.” (The Quarterly Review of Biology, 1 March 2012) "Bioinformatics researchers from many fields will find much value in this book." (Mathematical Reviews, 2011) "Experienced readers will find the review of advanced methods for bioinformatics challenging and attainable. This book will interest graduate students in statistics and bioinformatics researchers from many fields." (Book News, December 2009)Table of ContentsTable of Notation. 1 Bioinformatics and Gene Expression Experiments. 1.1 Introduction. 1.2 About This Book. 2 Basic Biology. 2.1 Background. 2.1.1 DNA Structures and Transcription. 2.2 Gene Expression Microarray Experiments. 3 Bayesian Linear Models for Gene Expression. 3.1 Introduction. 3.2 Bayesian Analysis of a Linear Model. 3.3 Bayesian Linear Models for Differential Expression. 3.4 Bayesian ANOVA for Gene Selection. 3.5 Robust ANOVA model with Mixtures of Singular Distributions. 3.6 Case Study. 3.7 Accounting for Nuisance Effects. 3.8 Summary and Further Reading. 4 Bayesian Multiple Testing and False Discovery Rate Analysis. 4.1 Introduction to Multiple Testing. 4.2 False Discovery Rate Analysis. 4.3 Bayesian False Discovery Rate Analysis. 4.4 Bayesian Estimation of FDR. 4.5 FDR and Decision Theory. 4.6 FDR and bFDR Summary. 5 Bayesian Classification for Microarray Data. 5.1 Introduction. 5.2 Classification and Discriminant Rules. 5.3 Bayesian Discriminant Analysis. 5.4 Bayesian Regression Based Approaches to Classification. 5.5 Bayesian Nonlinear Classification. 5.6 Prediction and Model Choice. 5.7 Examples. 5.8 Discussion. 6 Bayesian Hypothesis Inference for Gene Classes. 6.1 Interpreting Microarray Results. 6.2 Gene Classes. 6.3 Bayesian Enrichment Analysis. 6.4 Multivariate Gene Class Detection. 6.5 Summary. 7 Unsupervised Classification and Bayesian Clustering. 7.1 Introduction to Bayesian Clustering for Gene Expression Data. 7.2 Hierarchical Clustering. 7.3 K-Means Clustering. 7.4 Model-Based Clustering. 7.5 Model-Based Agglomerative Hierarchical Clustering. 7.6 Bayesian Clustering. 7.7 Principal Components. 7.8 Mixture Modeling. 7.8.1 Label Switching. 7.9 Clustering Using Dirichlet Process Prior. 7.9.1 Infinite Mixture of Gaussian Distributions. 8 Bayesian Graphical Models. 8.1 Introduction. 8.2 Probabilistic Graphical Models. 8.3 Bayesian Networks. 8.4 Inference for Network Models. 9 Advanced Topics. 9.1 Introduction. 9.2 Analysis of Time Course Gene Expression Data. 9.3 Survival Prediction Using Gene Expression Data. Appendix A: Basics of Bayesian Modeling. A.1 Basics. A.1.1 The General Representation Theorem. A.1.2 Bayes’ Theorem. A.1.3 Models Based on Partial Exchangeability. A.1.4 Modeling with Predictors. A.1.5 Prior Distributions. A.1.6 Decision Theory and Posterior and Predictive Inferences. A.1.7 Predictive Distributions. A.1.8 Examples. A.2 Bayesian Model Choice. A.3 Hierarchical Modeling. A.4 Bayesian Mixture Modeling. A.5 Bayesian Model Averaging. Appendix B: Bayesian Computation Tools. B.1 Overview. B.2 Large-Sample Posterior Approximations. B.2.1 The Bayesian Central Limit Theorem. B.2.2 Laplace’s Method. B.3 Monte Carlo Integration. B.4 Importance Sampling. B.5 Rejection Sampling. B.6 Gibbs Sampling. B.7 The Metropolis Algorithm and Metropolis–Hastings. B.8 Advanced Computational Methods. B.8.1 Block MCMC. B.8.2 Truncated Posterior Spaces. B.8.3 Latent Variables and the Auto-Probit Model. B.8.4 Bayesian Simultaneous Credible Envelopes. B.8.5 Proposal Updating. B.9 Posterior Convergence Diagnostics. B.10 MCMC Convergence and the Proposal. B.10.1 Graphical Checks for MCMC Methods. B.10.2 Convergence Statistics. B.10.3 MCMC in High-Throughput Analysis. B.11 Summary. References. Index.

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Book SynopsisThe latest edition of this highly successful textbook introduces the key techniques and concepts involved in cloning genes and in studying their expression and variation. The new edition features: Increased coverage of whole-genome sequencing technologies and enhanced treatment of bioinformatics. Clear, two-colour diagrams throughout. A dedicated website including all figures. Noted for its outstanding balance between clarity of coverage and level of detail, this book provides an excellent introduction to the fast moving world of molecular genetics.Trade Review“This third edition is absolutely necessary to incorporate the recent advances, such as genome sequencing, polymerase chain reaction, and microarray technology, in this field.” (Doody’s, 19 October 2012)Table of ContentsPreface xiii 1 From Genes to Genomes 1 1.1 Introduction 1 1.2 Basic molecular biology 4 1.2.1 The DNA backbone 4 1.2.2 The base pairs 6 1.2.3 RNA structure 10 1.2.4 Nucleic acid synthesis 11 1.2.5 Coiling and supercoilin 11 1.3 What is a gene? 13 1.4 Information flow: gene expression 15 1.4.1 Transcription 16 1.4.2 Translation 19 1.5 Gene structure and organisation 20 1.5.1 Operons 20 1.5.2 Exons and introns 21 1.6 Refinements of the model 22 2 How to Clone a Gene 25 2.1 What is cloning? 25 2.2 Overview of the procedures 26 2.3 Extraction and purification of nucleic acids 29 2.3.1 Breaking up cells and tissues 29 2.3.2 Alkaline denaturation 31 2.3.3 Column purification 31 2.4 Detection and quantitation of nucleic acids 32 2.5 Gel electrophoresis 33 2.5.1 Analytical gel electrophoresis 33 2.5.2 Preparative gel electrophoresis 36 2.6 Restriction endonucleases 36 2.6.1 Specificity 37 2.6.2 Sticky and blunt ends 40 2.7 Ligation 42 2.7.1 Optimising ligation conditions 44 2.7.2 Preventing unwanted ligation: alkaline phosphatase and double digests 46 2.7.3 Other ways of joining DNA fragments 48 2.8 Modification of restriction fragment ends 49 2.8.1 Linkers and adaptors 50 2.8.2 Homopolymer tailing 52 2.9 Plasmid vectors 53 2.9.1 Plasmid replication 54 2.9.2 Cloning sites 55 2.9.3 Selectable markers 57 2.9.4 Insertional inactivation 58 2.9.5 Transformation 59 2.10 Vectors based on the lambda bacteriophage 61 2.10.1 Lambda biology 61 2.10.2 In vitro packaging 65 2.10.3 Insertion vectors 66 2.10.4 Replacement vectors 68 2.11 Cosmids 71 2.12 Supervectors: YACs and BACs 72 2.13 Summary 73 3 Genomic and cDNA Libraries 75 3.1 Genomic libraries 77 3.1.1 Partial digests 77 3.1.2 Choice of vectors 80 3.1.3 Construction and evaluation of a genomic library 83 3.2 Growing and storing libraries 86 3.3 cDNA libraries 87 3.3.1 Isolation of mRNA 88 3.3.2 cDNA synthesis 89 3.3.3 Bacterial cDNA 93 3.4 Screening libraries with gene probes 94 3.4.1 Hybridization 94 3.4.2 Labelling probes 98 3.4.3 Steps in a hybridization experiment 99 3.4.4 Screening procedure 100 3.4.5 Probe selection and generation 101 3.5 Screening expression libraries with antibodies 103 3.6 Characterization of plasmid clones 106 3.6.1 Southern blots 107 3.6.2 PCR and sequence analysis 108 4 Polymerase Chain Reaction (PCR) 109 4.1 The PCR reaction 110 4.2 PCR in practice 114 4.2.1 Optimisation of the PCR reaction 114 4.2.2 Primer design 115 4.2.3 Analysis of PCR products 117 4.2.4 Contamination 118 4.3 Cloning PCR products 119 4.4 Long-range PCR 121 4.5 Reverse-transcription PCR 123 4.6 Quantitative and real-time PCR 123 4.6.1 SYBR Green 123 4.6.2 TaqMan 125 4.6.3 Molecular beacons 125 4.7 Applications of PCR 127 4.7.1 Probes and other modified products 127 4.7.2 PCR cloning strategies 128 4.7.3 Analysis of recombinant clones and rare events 129 4.7.4 Diagnostic applications 130 5 Sequencing a Cloned Gene 131 5.1 DNA sequencing 131 5.1.1 Principles of DNA sequencing 131 5.1.2 Automated sequencing 136 5.1.3 Extending the sequence 137 5.1.4 Shotgun sequencing; contig assembly 138 5.2 Databank entries and annotation 140 5.3 Sequence analysis 146 5.3.1 Identification of coding region 146 5.3.2 Expression signals 147 5.4 Sequence comparisons 148 5.4.1 DNA sequences 148 5.4.2 Protein sequence comparisons 151 5.4.3 Sequence alignments: Clustal 157 5.5 Protein structure 160 5.5.1 Structure predictions 160 5.5.2 Protein motifs and domains 162 5.6 Confirming gene function 165 5.6.1 Allelic replacement and gene knockouts 166 5.6.2 Complementation 168 6 Analysis of Gene Expression 169 6.1 Analysing transcription 169 6.1.1 Northern blots 170 6.1.2 Reverse transcription-PCR 171 6.1.3 In situ hybridization 174 6.2 Methods for studying the promoter 174 6.2.1 Locating the promoter 175 6.2.2 Reporter genes 177 6.3 Regulatory elements and DNA-binding proteins 179 6.3.1 Yeast one-hybrid assays 179 6.3.2 DNase I footprinting 181 6.3.3 Gel retardation assays 181 6.3.4 Chromatin immunoprecipitation (ChIP) 183 6.4 Translational analysis 185 6.4.1 Western blots 185 6.4.2 Immunocytochemistry and immunohistochemistry 187 7 Products from Native and Manipulated Cloned Genes 189 7.1 Factors affecting expression of cloned genes 190 7.1.1 Transcription 190 7.1.2 Translation initiation 192 7.1.3 Codon usage 193 7.1.4 Nature of the protein product 194 7.2 Expression of cloned genes in bacteria 195 7.2.1 Transcriptional fusions 195 7.2.2 Stability: conditional expression 198 7.2.3 Expression of lethal genes 201 7.2.4 Translational fusions 201 7.3 Yeast systems 204 7.3.1 Cloning vectors for yeasts 204 7.3.2 Yeast expression systems 206 7.4 Expression in insect cells: baculovirus systems 208 7.5 Mammalian cells 209 7.5.1 Cloning vectors for mammalian cells 210 7.5.2 Expression in mammalian cells 213 7.6 Adding tags and signals 215 7.6.1 Tagged proteins 215 7.6.2 Secretion signals 217 7.7 In vitro mutagenesis 218 7.7.1 Site-directed mutagenesis 218 7.7.2 Synthetic genes 223 7.7.3 Assembly PCR 223 7.7.4 Synthetic genomes 224 7.7.5 Protein engineering 224 7.8 Vaccines 225 7.8.1 Subunit vaccines 225 7.8.2 DNA vaccines 226 8 Genomic Analysis 229 8.1 Overview of genome sequencing 229 8.1.1 Strategies 230 8.2 Next generation sequencing (NGS) 231 8.2.1 Pyrosequencing (454) 232 8.2.2 SOLiD sequencing (Applied Biosystems) 235 8.2.3 Bridge amplification sequencing (Solexa/Ilumina) 237 8.2.4 Other technologies 239 8.3 De novo sequence assembly 239 8.3.1 Repetitive elements and gaps 240 8.4 Analysis and annotation 242 8.4.1 Identification of ORFs 243 8.4.2 Identification of the function of genes and their products 250 8.4.3 Other features of nucleic acid sequences 251 8.5 Comparing genomes 256 8.5.1 BLAST 256 8.5.2 Synteny 257 8.6 Genome browsers 258 8.7 Relating genes and functions: genetic and physical maps 260 8.7.1 Linkage analysis 261 8.7.2 Ordered libraries and chromosome walking 262 8.8 Transposon mutagenesis and other screening techniques 263 8.8.1 Transposition in bacteria 263 8.8.2 Transposition in Drosophila 266 8.8.3 Transposition in other organisms 268 8.8.4 Signature-tagged mutagenesis 269 8.9 Gene knockouts, gene knockdowns and gene silencing 271 8.10 Metagenomics 273 8.11 Conclusion 274 9 Analysis of Genetic Variation 275 9.1 Single nucleotide polymorphisms 276 9.1.1 Direct sequencing 278 9.1.2 SNP arrays 279 9.2 Larger scale variations 280 9.2.1 Microarrays and indels 281 9.3 Other methods for studying variation 282 9.3.1 Genomic Southern blot analysis: restriction fragment length polymorphisms (RFLPs) 282 9.3.2 VNTR and microsatellites 285 9.3.3 Pulsed-field gel electrophoresis 287 9.4 Human genetic variation: relating phenotype to genotype 289 9.4.1 Linkage analysis 289 9.4.2 Genome-wide association studies (GWAS) 292 9.4.3 Database resources 294 9.4.4 Genetic diagnosis 294 9.5 Molecular phylogeny 295 9.5.1 Methods for constructing trees 298 10 Post-Genomic Analysis 305 10.1 Analysing transcription: transcriptomes 305 10.1.1 Differential screening 306 10.1.2 Other methods: transposons and reporters 308 10.2 Array-based methods 308 10.2.1 Expressed sequence tag (EST) arrays 309 10.2.2 PCR product arrays 310 10.2.3 Synthetic oligonucleotide arrays 312 10.2.4 Important factors in array hybridization 313 10.3 Transcriptome sequencing 315 10.4 Translational analysis: proteomics 316 10.4.1 Two-dimensional electrophoresis 317 10.4.2 Mass spectrometry 318 10.5 Post-translational analysis: protein interactions 320 10.5.1 Two-hybrid screening 320 10.5.2 Phage display libraries 321 10.6 Epigenetics 323 10.7 Integrative studies: systems biology 324 10.7.1 Metabolomic analysis 324 10.7.2 Pathway analysis and systems biology 325 11 Modifying Organisms: Transgenics 327 11.1 Transgenesis and cloning 327 11.1.1 Common species used for transgenesis 328 11.1.2 Control of transgene expression 330 11.2 Animal transgenesis 333 11.2.1 Basic methods 333 11.2.2 Direct injection 333 11.2.3 Retroviral vectors 335 11.2.4 Embryonic stem cell technology 336 11.2.5 Gene knockouts 339 11.2.6 Gene knock-down technology: RNA interference 340 11.2.7 Gene knock-in technology 341 11.3 Applications of transgenic animals 342 11.4 Disease prevention and treatment 343 11.4.1 Live vaccine production: modification of bacteria and viruses 343 11.4.2 Gene therapy 346 11.4.3 Viral vectors for gene therapy 347 11.5 Transgenic plants and their applications 349 11.5.1 Introducing foreign genes 349 11.5.2 Gene subtraction 351 11.5.3 Applications 352 11.6 Transgenics: a coda 353 Glossary 355 Bibliography 375 Index 379

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Book SynopsisThe increase in the use of microarray technology has led to the need for good standards of microarray experimental notation, data representation, and the introduction of standard experimental controls, as well as standard data normalization and analysis techniques. This book covers the subject.Trade Review"I liked this book and would recommend it to any statistician new to microarray data analysis…a unique combination of features that make it a contender among the standard textbooks…" (Journal of the American Statistical Association, June 2006) "...clear...up-to-date...lively advice...an excellent reference text for any researcher interested in the analysis of transcriptomic data." (Short Book Reviews, Vol.25, No.1, April 2005) "...this is a very good introduction to one of the most widely used methods for assessing differential expression..." (Journal of the Royal Statistical Society, Vol 168 (4) 2005) "...presents a coherent and systematic overview of statistical methods in all stages of the process of analysing microarray data..." (Zentralblatt Math, Vol.1049, 2004)Table of ContentsPreface. 1 Preliminaries. 1.1 Using the R Computing Environment. 1.1.1 Installing smida. 1.1.2 Loading smida. 1.2 Data Sets from Biological Experiments. 1.2.1 Arabidopsis experiment: Anna Amtmann. 1.2.2 Skin cancer experiment: Nighean Barr. 1.2.3 Breast cancer experiment: John Bartlett. 1.2.4 Mammary gland experiment: Gusterson group. 1.2.5 Tuberculosis experiment: BµG@S group. I Getting Good Data. 2 Set-up of a Microarray Experiment. 2.1 Nucleic Acids: DNA and RNA. 2.2 Simple cDNA Spotted Microarray Experiment. 2.2.1 Growing experimental material. 2.2.2 Obtaining RNA. 2.2.3 Adding spiking RNA and poly-T primer. 2.2.4 Preparing the enzyme environment. 2.2.5 Obtaining labelled cDNA. 2.2.6 Preparing cDNA mixture for hybridization. 2.2.7 Slide hybridization. 3 Statistical Design of Microarrays. 3.1 Sources of Variation. 3.2 Replication. 3.2.1 Biological and technical replication. 3.2.2 How many replicates? 3.2.3 Pooling samples. 3.3 Design Principles. 3.3.1 Blocking, crossing and randomization. 3.3.2 Design and normalization. 3.4 Single-channelMicroarray Design. 3.4.1 Design issues. 3.4.2 Design layout. 3.4.3 Dealing with technical replicates. 3.5 Two-channelMicroarray Designs. 3.5.1 Optimal design of dual-channel arrays. 3.5.2 Several practical two-channel designs. 4 Normalization. 4.1 Image Analysis. 4.1.1 Filtering. 4.1.2 Gridding. 4.1.3 Segmentation. 4.1.4 Quantification. 4.2 Introduction to Normalization. 4.2.1 Scale of gene expression data. 4.2.2 Using control spots for normalization. 4.2.3 Missing data. 4.3 Normalization for Dual-channel Arrays. 4.3.1 Order for the normalizations. 4.3.2 Spatial correction. 4.3.3 Background correction. 4.3.4 Dye effect normalization. 4.3.5 Normalization within and across conditions. 4.4 Normalization of Single-channel Arrays. 4.4.1 Affymetrix data structure. 4.4.2 Normalization of Affymetrix data. 5 Quality Assessment. 5.1 Using MIAME in Quality Assessment. 5.1.1 Components of MIAME. 5.2 Comparing Multivariate Data. 5.2.1 Measurement scale. 5.2.2 Dissimilarity and distance measures. 5.2.3 Representing multivariate data. 5.3 Detecting Data Problems. 5.3.1 Clerical errors. 5.3.2 Normalization problems. 5.3.3 Hybridization problems. 5.3.4 Array mishandling. 5.4 Consequences of Quality Assessment Checks. 6 Microarray Myths: Data. 6.1 Design. 6.1.1 Single-versus dual-channel designs? 6.1.2 Dye-swap experiments. 6.2 Normalization. 6.2.1 Myth: ‘microarray data is Gaussian’. 6.2.2 Myth: ‘microarray data is not Gaussian’. 6.2.3 Confounding spatial and dye effect. 6.2.4 Myth: ‘non-negative background subtraction’. II Getting Good Answers. 7 Microarray Discoveries. 7.1 Discovering Sample Classes. 7.1.1 Why cluster samples? 7.1.2 Sample dissimilarity measures. 7.1.3 Clustering methods for samples. 7.2 Exploratory Supervised Learning. 7.2.1 Labelled dendrograms. 7.2.2 Labelled PAM-type clusterings. 7.3 Discovering Gene Clusters. 7.3.1 Similarity measures for expression profiles. 7.3.2 Gene clustering methods. 8 Differential Expression. 8.1 Introduction. 8.1.1 Classical versus Bayesian hypothesis testing. 8.1.2 Multiple testing ‘problem’. 8.2 Classical Hypothesis Testing. 8.2.1 What is a hypothesis test? 8.2.2 Hypothesis tests for two conditions. 8.2.3 Decision rules. 8.2.4 Results from skin cancer experiment. 8.3 Bayesian Hypothesis Testing. 8.3.1 A general testing procedure. 8.3.2 Bayesian t-test. 9 Predicting Outcomes with Gene Expression Profiles. 9.1 Introduction. 9.1.1 Probabilistic classification theory. 9.1.2 Modelling and predicting continuous variables. 9.2 Curse of Dimensionality: Gene Filtering. 9.2.1 Use only significantly expressed genes. 9.2.2 PCA and gene clustering. 9.2.3 Penalized methods. 9.2.4 Biological selection. 9.3 Predicting ClassMemberships. 9.3.1 Variance-bias trade-off in prediction. 9.3.2 Linear discriminant analysis. 9.3.3 k-nearest neighbour classification. 9.4 Predicting Continuous Responses. 9.4.1 Penalized regression: LASSO. 9.4.2 k-nearest neighbour regression. 10 Microarray Myths: Inference. 10.1 Differential Expression. 10.1.1 Myth: ‘Bonferroni is too conservative’. 10.1.2 FPR and collective multiple testing. 10.1.3 Misinterpreting FDR. 10.2 Prediction and Learning. 10.2.1 Cross-validation. Bibliography. Index.

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Book SynopsisThe field of whole genome selection has quickly developed into the breeding methodology of the future. As efforts to map a wide variety of animal genomes have matured and full animal genomes are now available for many animal scientists and breeders are looking to apply these techniques to livestock production.Trade Review"Genomic Selection in Animals is a well-written book by a leading animal quantitative geneticist...This book will be particularly useful for graduate students in animal breeding and genetics, and more broadly for professionals with an interest in understanding how genomic information is being incorporated into breeding programs...Overall, this book is a readable summary of the concepts and current methods underlying genomic selection and a useful reference that I recommend for those with an interest in this rapidly evolving field." (Journal of the American Veterinary Medical Association 15/03/2017)Table of ContentsPreface: Welcome to the “Promised Land” xiii Chapter 1 Historical Overview 1 Introduction 1 The Mendelian Theory of Genetics 1 The Mendelian Basis of Quantitative Variation 2 Detection of QTL with Morphological and Biochemical Markers 2 DNA-Level Markers, 1974–1994 3 DNA-Level Markers Since 1995: SNPs and CNV 4 QTL Detection Prior to Genomic Selection 4 MAS Prior to Genomic Selection 5 Summary 6 Chapter 2 Types of Current Genetic Markers and Genotyping Methodologies 7 Introduction 7 From Biochemical Markers to DNA]Level Markers 7 DNA Microsatellites 8 Single Nucleotide Polymorphisms 8 Copy Number Variation 9 Complete Genome Sequencing 9 Summary 10 Chapter 3 Advanced Animal Breeding Programs Prior to Genomic Selection 11 Introduction 11 Within a Breed Selection: Basic Principles and Equations 11 Traditional Selection Schemes for Dairy Cattle 12 Crossbreeding Schemes: Advantages and Disadvantages 14 Summary 15 Chapter 4 Economic Evaluation of Genetic Breeding Programs 17 Introduction 17 National Economy versus Competition among Breeders 17 Criteria for Economic Evaluation: Profit Horizon, Interest Rate, and Return on Investment 18 Summary 20 Chapter 5 Least Squares, Maximum Likelihood, and Bayesian Parameter Estimation 21 Introduction 21 Least Squares Parameter Estimation 21 ML Estimation for a Single Parameter 22 ML Multiparameter Estimation 24 Methods to Maximize Likelihood Functions 26 Confidence Intervals and Hypothesis Testing for MLE 26 Bayesian Estimation 27 Parameter Estimation via the Gibbs Sampler 28 Summary 29 Chapter 6 Trait-Based Genetic Evaluation: The Mixed Model 31 Introduction 31 Principles of Selection Index 31 The Mixed Linear Model 34 The Mixed Model Equations 34 Solving the Mixed Model Equations 35 Important Properties of Mixed Model Solutions 36 Multivariate Mixed Model Analysis 37 The Individual Animal Model 38 Yield Deviations and Daughter Yield Deviations 39 Analysis of DYD as the Dependent Variable 40 Summary 41 Chapter 7 Maximum Likelihood and Bayesian Estimation of QTL Parameters with Random Effects Included in the Model 43 Introduction 43 Maximum Likelihood Estimation of QTL Effects with Random Effects Included in the Model, the Daughter Design 43 The Granddaughter Design 45 Determination of Prior Distributions of the QTL Parameters for the Granddaughter Design 46 Formula for Bayesian Estimation and Tests of Significance of a Segregating QTL in a Granddaughter Design 49 Summary 50 Chapter 8 Maximum Likelihood, Restricted Maximum Likelihood, and Bayesian Estimation for Mixed Models 51 Introduction 51 Derivation of Solutions to the Mixed Model Equations by Maximum Likelihood 51 Estimation of the Mixed Model Variance Components 52 Maximum Likelihood Estimation of Variance Components 52 Restricted Maximum Likelihood Estimation of Variance Components 54 Estimation of Variance Components via the Gibbs Sampler 55 Summary 58 Chapter 9 Distribution of Genetic Effects, Theory, and Results 59 Introduction 59 Modeling the Polygenic Variance 59 The Effective Number of QTL 61 The Case of the Missing Heritability 61 Methods for Determination of Causative Mutations for QTL in Animals and Humans 62 Determination of QTN in Dairy Cattle 63 Estimating the Number of Segregating QTL Based on Linkage Mapping Studies 64 Results of Genome Scans of Dairy Cattle by Granddaughter Designs 65 Results of Genome]Wide Association Studies in Dairy Cattle by SNP Chips 66 Summary 66 Chapter 10 The Multiple Comparison Problem 69 Introduction 69 Multiple Markers and Whole Genome Scans 69 QTL Detection by Permutation Tests 71 QTL Detection Based on the False Discovery Rate 71 A Priori Determination of the Proportion of False Positives 74 Biases with Estimation of Multiple QTL 75 Bayesian Estimation of QTL from Whole Genome Scans: Theory 76 Bayes A and Bayes B Models 77 Bayesian Estimation of QTL from Whole Genome Scans: Simulation Results 79 Summary 80 Chapter 11 Linkage Mapping of QTL 81 Introduction 81 Interval Mapping by Nonlinear Regression: The Backcross Design 81 Interval Mapping for Daughter and Granddaughter Designs 83 Computation of Confidence Intervals 84 Simulation Studies of CIs 85 Empirical Methods to Estimate CIs, Parametric and Nonparametric Bootstrap, and Jackknife Methods 86 Summary 87 Chapter 12 Linkage Disequilibrium Mapping of QTL 89 Introduction 89 Estimation of Linkage Disequilibrium in Animal Populations 89 Linkage Disequilibrium QTL Mapping: Basic Principles 90 Joint Linkage and Linkage Disequilibrium Mapping 92 Multitrait and Multiple QTL LD Mapping 93 Summary 93 Chapter 13 Marker-Assisted Selection: Basic Strategies 95 Introduction 95 Situations in Which Selection Index is Inefficient 95 Potential Contribution of MAS for Selection within a Breed: General Considerations 96 Phenotypic Selection versus MAS for Individual Selection 97 MAS for Sex-Limited Traits 98 MAS Including Marker and Phenotypic Information on Relatives 99 Maximum Selection Efficiency of MAS with All QTL Known, Relative to Trait-Based Selection, and the Reduction in RSE Due to Sampling Variance 99 Marker Information in Segregating Populations 100 Inclusion of Marker Information in “Animal Model” Genetic Evaluations 100 Predicted Genetic Gains with Genomic Estimated Breeding Values: Results of Simulation Studies 101 Summary 102 Chapter 14 Genetic Evaluation Based on Dense Marker Maps: Basic Strategies 103 Introduction 103 The Basic Steps in Genomic Evaluation 103 Evaluation of Genomic Estimated Breeding Values 104 Sources of Bias in Genomic Evaluation 104 Marker Effects Fixed or Random? 105 Individual Markers versus Haplotypes 106 Total Markers versus Usable Markers 106 Deviation of Genotype Frequencies from Their Expectations 107 Inclusion of All Markers versus Selection of Markers with Significant Effects 107 The Genomic Relationship Matrix 108 Summary 109 Chapter 15 Genetic Evaluation Based on Analysis of Genetic Evaluations or Daughter-Yield Evaluations 111 Introduction 111 Comparison of Single]Step and Multistep Models 111 Derivation and Properties of Daughter Yields and DYD 112 Computation of “Deregressed” Genetic Evaluations 113 Analysis of DYD as the Dependent Variable with All Markers Included as Random Effects 114 Computation of Reliabilities for Genomic Estimated Breeding Values 116 Bayesian Weighting of Marker Effects 116 Additional Bayesian Methods for Genomic Evaluation 117 Summary 117 Chapter 16 Genomic Evaluation Based on Analysis of Production Records 119 Introduction 119 Single-Step Methodologies: The Basic Strategy 119 Computation of the Modified Relationship Matrix when only a Fraction of the Animals are Genotyped: The Problem 120 Criteria for Valid Genetic Relationship Matrices 120 Computation of the Modified Relationship Matrix when only a Fraction of the Animals are Genotyped, the Solution 121 Solving the Mixed Model Equations without Inverting H 121 Inverting the Genomic Relationship Matrix 122 Estimation of Reliabilities for Genomic Breeding Values Derived by Single]Step Methodologies 122 Single-Step Computation of Genomic Evaluations with Unequally Weighted Marker Effects 123 Summary 124 Chapter 17 Validation of Methods for Genomic Estimated Breeding Values 125 Introduction 125 Criteria for Evaluation of Estimated Genetic Values 125 Methods Used to Validate Genomic Genetic Evaluations 126 Evaluation of Two-Step Methodology Based on Simulated Dairy Cattle Data 127 Evaluation of Multistep Methodology Based on Actual Dairy Cattle Data 127 Evaluation of Single-Step Methodologies Based on Actual Dairy Cattle Data 128 Evaluation of Single- and Multistep Methodologies Based on Actual Poultry Data 129 Evaluation of Single- and Multistep Methodologies Based on Actual Swine Data 130 Evaluation of GEBV for Plants Based on Actual Data 130 Summary 131 Chapter 18 By-Products of Genomic Analysis: Pedigree Validation and Determination 133 Introduction 133 The Effects of Incorrect Parentage Identification on Breeding Programs 133 Principles of Parentage Verification and Identification with Genetic Markers 134 Paternity Validation Prior to High]Density SNP Chips 135 Paternity Validation and Determination with SNP Chips 135 Validation of More Distant Relationships 136 Pedigree Reconstruction with High]Density Genetic Markers 137 Summary 137 Chapter 19 Imputation of Missing Genotypes: Methodologies, Accuracies, and Effects on Genomic Evaluations 139 Introduction 139 Determination of Haplotypes for Imputation 139 Imputation in Humans versus Imputation in Farm Animals 140 Algorithms Proposed for Imputation in Human and Animal Populations 141 Comparisons of Accuracy and Speed of Imputation Methods 142 Effect of Imputation on Genomic Genetic Evaluations 143 Summary 144 Chapter 20 Detection and Validation of Quantitative Trait Nucleotides 145 Introduction 145 GWAS for Economic Traits in Commercial Animals 146 Detection of QTN: Is It Worth the Effort? 146 QTN Determination in Farm Animals: What Constitutes Proof? 147 Concordance between DNA-Level Genotypes and QTL Status 148 Determination of Concordance by the “APGD” 148 Determination of Phase for Grandsires Heterozygous for the QTL 149 Determination of Recessive Lethal Genes by GWAS and Effects Associated with Heterozygotes 150 Verification of QTN by Statistical and Biological Methods 150 Summary 151 Chapter 21 Future Directions and Conclusions 153 Introduction 153 More Markers versus More Individuals with Genotypes 153 Computation of Genomic Evaluations for Cow and Female Calves 154 Improvement of Genomic Evaluation Methods 154 Long-Term Considerations 155 Weighting Evaluations of Old versus Young Bulls 156 Direct Genetic Manipulation in Farm Animals 156 Velogenetics: The Synergistic Use of MAS and Germ-Line Manipulation 157 Summary 157 References 159 Index 171

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Book SynopsisThis up-to-date review of seed genomics, from basic seed biology to practical applications in crop science, provides a thorough background understanding of seed biology from a basic science perspective.Table of ContentsContributors xi Introduction 1 Philip W. Becraft Chapter 1 Large-Scale Mutant Analysis of Seed Development in Arabidopsis 5 David W. Meinke Introduction 5 Historical Perspective 5 Arabidopsis Embryo Mutant System 7 Large-Scale Forward Genetic Screens for Seed Mutants 7 Approaches to Mutant Analysis 8 Strategies for Approaching Saturation 10 SeedGenes Database of Essential Genes in Arabidopsis 11 Embryo Mutants with Gametophyte Defects 13 General Features of EMB Genes in Arabidopsis 14 Value of Large Datasets of Essential Genes 15 Directions for Future Research 16 Acknowledgments 17 References 17 Chapter 2 Embryogenesis in Arabidopsis: Signaling, Genes, and the Control of Identity 21 D. L. C. Kumari Fonseka, Xiyan Yang, Anna Mudge, Jennifer F. Topping, and Keith Lindsey Introduction 21 Cellular Events 21 Genes and Signaling – the Global Picture 23 Coordination of Genes and Cellular Processes: a Role for Hormones 25 Genes and Pattern 30 Conclusion and Future Directions 36 References 36 Chapter 3 Endosperm Development 43 Odd-Arne Olsen and Philip W. Becraft Introduction 43 Overview of Endosperm Structure and Development 43 Endosperm Cell Fate Specification and Differentiation 48 Genomic Resources 53 Transcriptional Profiling of Endosperm Development 54 Gene Imprinting in Cereal Endosperm 56 Conclusion 57 Acknowledgments 58 References 58 Chapter 4 Epigenetic Control of Seed Gene Imprinting 63 Christian A. Ibarra, Jennifer M. Frost, Juhyun Shin, Tzung-Fu Hsieh, and Robert L. Fischer Introduction 63 Genomic Imprinting and Parental Conflict Theory 63 Epigenetic Regulators of Arabidopsis Imprinting 65 Mechanisms Establishing Arabidopsis Gene Imprinting 69 Imprinting in the Embryo 74 Imprinting in Monocots 75 Evolution of Plant Imprinting 77 Conclusion 78 Acknowledgments 78 References 78 Chapter 5 Apomixis 83 Anna M. G. Koltunow, Peggy Ozias-Akins, and Imran Siddiqi Introduction 83 Biology of Apomixis in Natural Systems 84 Phylogenetic and Geographical Distribution of Apomixis 89 Inheritance of Apomixis 90 Genetic Diversity in Natural Apomictic Populations 93 Molecular Relationships between Sexual and Apomictic Pathways 94 Features of Chromosomes Carrying Apomixis Loci and Implications for Regulation of Apomixis 95 Genes Associated with Apomixis 96 Transferring Apomixis to Sexual Plants: Clues from Apomicts 97 Synthetic Approach to Building Apomixis 98 Synthetic Clonal Seed Formation 102 Conclusion and Future Prospects 103 References 103 Chapter 6 High-Throughput Genetic Dissection of Seed Dormancy 111 Jose M. Barrero, Colin Cavanagh, and Frank Gubler Introduction 111 Profiling of Transcriptomic Changes 113 Use of New Sequencing Platforms and Associated Techniques to Study Seed Dormancy 114 Visualization Tools 116 Coexpression Studies and Systems Biology Approaches 116 Mapping Populations for Gene Discovery 117 Perspective 118 Acknowledgments 119 References 119 Chapter 7 Genomic Specification of Starch Biosynthesis in Maize Endosperm 123 Tracie A. Hennen-Bierwagen and Alan M. Myers Introduction 123 Overview of Starch Biosynthetic Pathway 124 Genomic Specification of Endosperm Starch Biosynthesis in Maize 126 Conclusion 134 References 134 Chapter 8 Evolution, Structure, and Function of Prolamin Storage Proteins 139 David Holding and Joachim Messing Introduction 139 Prolamin Multigene Families 139 Endosperm Texture and Storage of Prolamins 143 Conclusion 154 References 154 Chapter 9 Improving Grain Quality: Wheat 159 Peter R. Shewry Introduction 159 Grain Structure and Composition 159 End Use Quality 161 Redesigning the Grain 163 Manipulation of Grain Protein Content and Quality 163 Manipulation of Grain Texture 167 Development of Wheat with Resistant Starch 168 Improving Content and Composition of Dietary Fiber 169 Wheat Grain Cell Walls 169 Conclusion 173 Acknowledgments 173 References 173 Chapter 10 Legume Seed Genomics: How to Respond to the Challenges and Potential of a Key Plant Family? 179 Mélanie Noguero, Karine Gallardo, Jérôme Verdier, Christine Le Signor, Judith Burstin, and Richard Thompson Introduction 179 Development of Genomics Tools 180 Applications of Genomics Tools to Legume Seed Biology 185 Future Challenges 192 References 193 Chapter 11 Cotton Fiber Genomics 203 Xueying Guan and Z. Jeffrey Chen Introduction 203 Cotton Fiber Development 204 Roles for Transcription Factors in Development of Arabidopsis Leaf Trichomes, Seed Hairs, and Cotton Fibers 204 Fiber Cell Expansion through Cell Wall Biosynthesis 208 Regulation of Phytohormones during Cotton Fiber Development 209 Cotton Fiber Genes in Diploid and Tetraploid Cotton 210 Roles for Small RNAs in Cotton Fiber Development 211 Conclusion 212 References 213 Chapter 12 Genomic Changes in Response to 110 Cycles of Selection for Seed Protein and Oil Concentration in Maize 217 Christine J. Lucas, Han Zhao, Martha Schneerman, and Stephen P. Moose Introduction 217 Background on the Illinois Long-Term Selection Experiment 217 Phenotypic Responses to Selection 219 Additional Traits Affected by Selection 220 Unlimited Genetic Variation? 221 Genetic Response to Selection: QTL Mapping in the Crosses of IHP x ILP and IHO x ILO 222 New Mapping Population: Illinois Protein Strain Recombinant Inbreds 223 Characterization of Zein Genes and Their Expression in Illinois Protein Strains 225 Contribution of Zein Regulatory Factor Opaque2 to Observed Responses to Selection in Illinois Protein Strains 227 Major Effect QTL May Explain IRHP Phenotype 228 Zein Promoter-Reporter Lines to Investigate Regulation of 22-kDa α-Zein Gene Expression in Illinois Protein Strains 229 Regulatory Changes in FL2-mRFP Expression When Crossed to Illinois Protein Strains 230 Regulation of FL2-mRFP 232 Acknowledgments 233 References 234 Chapter 13 Machine Vision for Seed Phenomics 237 Jeffery L. Gustin and A. Mark Settles Introduction 237 High-Energy Imaging: X-ray Tomography and Fluorescence 238 Optical Imaging: Visible Spectrum 240 Resonance Absorption: Infrared Spectrum 242 Resonance Emission: Nuclear Magnetic Resonance 245 Conclusion 246 Acknowledgments 246 References 246 Color plate section found between pages 42 and 43. Index 253

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Book SynopsisPolyploidy plays an important role in biological diversity, trait improvement, and plant species survival. Understanding the evolutionary phenomenon of polyploidy is a key challenge for plant and crop scientists.Table of ContentsContributors xi Preface xvii Section I Genomics of Hybrids 1 1 Yeast Hybrids and Polyploids as Models in Evolutionary Studies 3 Avraham A. Levy, Itay Tirosh, Sharon Reikhav, Yasmin Bloch, and Naama Barkai 2 Transcriptome Profiling of Drosophila Interspecific Hybrids: Insights into Mechanisms of Regulatory Divergence and Hybrid Dysfunction 15 Jos´e M. Ranz, Shu-Dan Yeh, Kevin G. Nyberg, and Carlos A. Machado 3 cis- and trans-Regulation in Drosophila Interspecific Hybrids 37 Joseph D. Coolon and Patricia J. Wittkopp 4 Gene Expression and Heterosis in Maize Hybrids 59 Mei Guo and J. Antoni Rafalski 5 Integrating “Omics” Data and Expression QTL to Understand Maize Heterosis 85 Camille Rustenholz and Patrick S. Schnable 6 Genomics and Heterosis in Hexaploid Wheat 105 Zhongfu Ni, Yingyin Yao, Huiru Peng, Zhaorong Hu, and Qixin Sun 7 Progress of Genomics and Heterosis Studies in Hybrid Rice 117 Lei Zhang, Yonggang Peng, Yang Dong, Hongtao Li, Wen Wang, and Zhen Zhu 8 Heterosis: The Case for Single-Gene Overdominance 137 Katie L. Liberatore, Ke Jiang, Dani Zamir, and Zachary B. Lippman Section II Genomics of Polyploids 153 9 Genomics and Transcriptomics of Photosynthesis in Polyploids 155 Jeremy E. Coate and Jeff J. Doyle 10 Chromosomal and Gene Expression Changes in Brassica Allopolyploids 171 Eric Jenczewski, A.M. Ch`evre, and K. Alix 11 Dynamics of Duplicated Gene Expression in Polyploid Cotton 187 Keith L. Adams and Jonathan F. Wendel 12 Reprogramming of Gene Expression in the Genetically Stable Bread Allohexaploid Wheat 195 Dominique Arnaud, Houda Chelaifa, Joseph Jahier, and Boulos Chalhoub 13 Nucleocytoplasmic Interaction Hypothesis of Genome Evolution and Speciation in Polyploid Plants Revisited: Polyploid Species-Specific Chromosomal Polymorphisms inWheat 213 Bikram S. Gill and B. Friebe Section III Mechanisms for Novelty in Hybrids and Polyploids 223 14 Genes Causing Postzygotic Hybrid Incompatibility in Plants: A Window into Co-Evolution 225 Kirsten Bomblies 15 Meiosis in Polyploids 241 Graham Moore 16 Genomic Imprinting: Parental Control of Gene Expression in Higher Plants 257 Peter C. McKeown, Antoine Fort, and Charles Spillane 17 Seed Development in Interploidy Hybrids 271 Roderick J. Scott, Julia L. Tratt, and Ahmed Bolbol 18 Chromatin and Small RNA Regulation of Nucleolar Dominance 291 Pedro Costa-Nunes and Olga Pontes 19 Genetic Rules of Heterosis in Plants 313 James A. Birchler 20 Chromatin and Gene Expression Mechanisms in Hybrids 323 Guangming He and Xing-Wang Deng 21 Genetic and Epigenetic Mechanisms for Polyploidy and Hybridity 335 Z. Jeffrey Chen and Helen H. Yu Index 355 A color plate is located between pages 174 and 175.

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Book SynopsisFully integrated and comprehensive in its coverage, Root Genomics and Soil Interactions examines the use of genome-based technologies to understand root development and adaptability to biotic and abiotic stresses and changes in the soil environment.Table of ContentsContributors ix Preface xv Chapter 1 Genomics of Root Development 3Boris Parizot and Tom Beeckman Introduction 3 Genomics of LRI 7 Rise of New Technologies to Understand Lateral Root Development 19 ComparativOmics, the Future 20 Acknowledgments 21 References 21 Chapter 2 The Complex Eukaryotic Transcriptome: Nonprotein-Coding RNAs and Root Development 29F. Ariel, A.B. Moreno, F. Bardou, and M. Crespi Genomic Approaches Reveal Novel Aspects of the Eukaryotic Transcriptome 29 The Role of RNA-Binding Proteins in npcRNA Metabolism and Activity 34 Nonprotein-Coding RNAs in Root Development 38 Future Perspectives 42 Acknowledgments 42 References 42 Chapter 3 Genomics of Auxin Action in Roots 49Elisabeth L. Williams and Ive De Smet Introduction 49 The Basis of Auxin Biology 49 Auxin Genomics in Root Development 55 Auxin and Root Hair Development 56 Auxin in Gravitropism 57 Auxin in LR Initiation 57 Conclusion 58 Acknowledgments 58 References 58 Chapter 4 Cell-Type Resolution Analysis of Root Development and Environmental Responses 63Jose R. Dinneny Introduction 63 Tools for Cell-Type Resolution Analysis 64 Analysis of Spatiotemporal Expression Patterns in the Arabidopsis Root 69 Analysis of Cell-Type-Specific Expression Patterns in the Rice Root 70 Cell-Type-Specific Analysis of Auxin 71 Cell-Type-Specific Analyses of Chromatin 71 Cell-Type-Specific Analyses of Responses to Environmental Change 72 Future Prospects 76 Acknowledgments 76 References 77 Chapter 5 Toward a Virtual Root: Interaction of Genomics and Modeling to Develop Predictive Biology Approaches 79Julien Lavenus, Leah Band, Alistair Middleton, Michael Wilson, Mikael Lucas, Laurent Laplaze, and Malcolm Bennett Assembling Root Gene Regulatory Pathways Using Genomics 79 Modeling Well-Characterized Small Root Gene Regulatory Networks 81 Building New Large-Scale Root Gene Regulatory Network 84 Multi-Scale Modeling Approaches to Study Root Growth and Development 88 Conclusions and Future Challenges 89 References 91 Chapter 6 Genomics of Root Hairs 93Hyung-Taeg Cho Genomics with Single Cells 93 Root Hair Development 94 High-Throughput Approaches for the Characterization of Root Hairs 95 Functions of Root Hair-Specific Genes 103 The Regulatory Pathway for Root Hair-Specific Genes 110 Perspective 111 Acknowledgments 111 References 112 Chapter 7 The Effects of Moisture Extremes on Plant Roots and Their Connections with Other Abiotic Stresses 117Laura M. Vaughn and Henry T. Nguyen Introduction 117 Low Water Availability—Drought 118 Excess Water—Soil Waterlogging, Flooding, and Submergence 128 Common Plant Root Responses to Abiotic Stressors 135 Continuing Challenges in Breeding for Plant Root Systems Tolerant to Abiotic Stress 137 Acknowledgments 138 References 138 Chapter 8 Legume Roots and Nitrogen-Fixing Symbiotic Interactions 145Philippe Laporte, Andreas Niebel, and Florian Frugier Genetic Dissection of the Legume Root System 145 Functional Genomic Analyses of Legume Nodules and Roots 155 Concluding Remarks 161 Acknowledgments 162 References 162 Chapter 9 What the Genomics of Arbuscular Mycorrhizal Symbiosis Teaches Us about Root Development 171Damien Formey, Cyril Jourda, Christophe Roux, and Pierre-Marc Delaux Forward and Reverse Genetics for Identifying Myc Mutants 172 Comparative Transcriptomics of AM Symbiosis: Toward Identification of Genes Involved in Root Development 175 Small RNAs in AM Symbiosis 181 Acknowledgments 183 References 183 Chapter 10 How Pathogens Affect Root Structure 189Michael Quentin, Tarek Hewezi, Isabelle Damiani, Pierre Abad, Thomas Baum, and Bruno Favery Introduction 189 Root Infection and Feeding Cell Ontogenesis 190 Genome-Wide Analysis of the Plant Response to Infection 192 The Plant Cytoskeleton Is Targeted by Root Pathogens 193 Root Pathogens Hijack Cell Cycle Regulators 194 Severe Cell Wall Remodeling Is Associated with Feeding Site Formation 195 Phytohormones Regulating Development and Defense May Control Feeding Site Formation 196 Role of miRNAs in Feeding Site Formation and Function 198 Nematode Effectors That Alter Root Cell Development during Parasitism 200 Conclusion 203 Acknowledgments 204 References 204 Chapter 11 Genomics of the Root—Actinorhizal Symbiosis 211Valerie Hocher, Nicole Alloisio, Laurent Laplaze, Didier Bogusz, and Philippe Normand Introduction 211 Actinorhizal Symbiosis 212 Development of Actinorhizal Nodules 214 Genomic Resources for Studying Actinorhizal Symbiosis 217 What Did We Learn from Actinorhizal Genomics? 220 Conclusion and Future Directions 222 Acknowledgments 222 References 223 Chapter 12 Plant Growth Promoting Rhizobacteria and Root Architecture 227Thais L.G. Carvalho, Paulo C.G. Ferreira, and Adriana S. Hemerly Introduction 227 Different Root Niches for PGPR Colonization 228 PGPR Recognition by Plants 229 Modulation of Root Growth and Architecture by PGPRs 232 Mechanisms of Plant Growth Promotion by PGPRs 234 Plant Genetic Programs Controlling Modulation of Root Growth and Architecture by PGPRs 240 Conclusions 241 Acknowledgments 242 References 242 Chapter 13 Translational Root Genomics for Crop Improvement 249Reyazul Rouf Mir, Mahendar Thudi, Siva K. Chamarthi, L. Krishnamurthy, Pooran M. Gaur, and Rajeev K. Varshney Introduction 249 Molecular Dissection of Root Trait 258 Molecular Breeding for Root Traits 259 Summary and Outlook 260 Acknowledgments 260 References 260 Index 265

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Book SynopsisThis book describes comprehensive step-by-step protocols for the delineation of genetic amplification and histological detection techniques. Each procedure has been tested and validated for its sensitivity, precision, and reproducibility, and the authors give advice on the design of primers for PCR applications and on optimizing these protocols for use with plant, insect, and prokaryotic cells.Table of ContentsOverview; Review of the PCR Technique; Preliminary Solution-Based Reactions; Preparation of Glass Slides and Tissues; In Situ PCR: DNA and RNA Targets; Special Applications of In Situ Amplification; Hybridization Reactions; Validation and Controls; Materials and Methods; Select Biography; Appendices; Index; About the Authors.

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Book SynopsisThis text aims to address the need for investigators to understand the basic concepts, as well as the practical concerns, associated with the use of antisense oligonucleotides in modifying gene expression.Trade Review"This book will be most useful to workers in the field and advanced graduate students." --The Quarterly Review of Biology, June 1999Table of ContentsCHEMISTRY, OLD AND NEW. Oligonucleoside Methylphosphonates: Synthesis and Properties (P. Miller). Oligo(Nucleoside Phosphorothioate)s (P. Guga, et al.). Modified Oligodeoxynucleotides as Antisense Therapeutics (P. Seeberger & M. Caruthers). Novel Chemistry (K. Altmann, et al.). OLIGONUCLEOTIDE INTERNALIZATION, MECHANISM OF ACTION, AND NON-SEQUENCE SPECIFICITY. Cellular Uptake and Biodistribution of Oligodeoxynucleotides (B. Hanss, et al.). Use of Cationic Lipid Complexes for Antisense Oligonucleotide Delivery (C. Bennett). Nonantisense Effects of Antisense Oligonucleotides (L. Neckers & K. Iyer). Ribonuclease H-Mediated Antisense Effects of Oligonucleotides and Controls for Antisense Experiments (D. Tidd). SEQUENCE-SPECIFIC INHIBITION OF GENE EXPRESSION. The Development of C-5 Propyne Oligonucleotides as Inhibitors of Gene Function (W. Flanagan & R. Wagner). The Use of Antisense Oligonucleotides to Inhibit Expression of Isozymes of Protein Kinase C (N. Dean, et al.). BCR-ABL as a Target for Antisense Intervention (S. O'Brien & T. Smetsers). The NF-kB Transcription Factor (R. Narayanan). Disruption of the Map Kinase Signaling Pathway Using Antisense Oligonucleotide Inhibitors Targeted to RAS and RAF Kinase (B. Monia). Protein Kinase A-Directed Antisense Blockade of Cancer Growth: Single Gene-Based Therapeutic Approach (Y. Cho-Chung). Use of Antisense Oligonucleotides in the Central Nervous System: Why Such Success? (M. McCarthy). APPLIED ANTISENSE OLIGONUCLEOTIDE THERAPEUTICS. Perturbing Hematopoietic Cell Gene Expression with Oligodeoxynucleotides: Research and Clinical Applications (A. Gewirtz & M. Ratajczak). BCL2 (B. Jansen & B. Brown). Biological Activity of Guanosine Quartet-Forming Oligonucleotides (R. Rando & M. Hogan). OLIGONUCLEOTIDES AS ANTI-HIV AGENTS. Perspectives on Antisense Technology Against HIV (J. Gee, et al.). In Vivo Pharmacokinetics of Oligonucleotides (S. Agrawal). Early Clinical Trails with Gem 91, A Systemic Oligodeoxynucleotide (R. Martin). THERAPEUTIC OLIGONUCLEOTIDE DATA BASE: PHARMACOKINETICS, IMMUNE STIMULATION, AND USE AS ANTIRESTENOTIC AGENTS. Antisense Therapy to Inhibit Angioplasty Restenosis (L. Rabbani & W. Wang). Pharmacokinetics of Oligonucleotides: A Review of Current Knowledge and Issues for the Future (T. Wallace & P. Cossum). Leukocyte Stimulation by Oligodeoxynucleotides (A. Krieg). SELECTED OLIGODEOXYNUCLEOTIDE DEVELOPMENTAL TOPICS: SPLICING AND TRIPLEXES. Modification of Alternative Splicing of Pre-mRNA by Antisense Oligonucleotides (R. Kole). Gene-Targeted Triple-Helix-Forming Oligonucleotides (F. Svinarchuk & C. Malvy). Triplex-Forming Oligonucleotides for Genetic Manipulation: An Alternative View (A. Faruqi & P. Glazer). A REVIEW OF RIBOZYME TECHNOLOGY. Therapeutic Ribozymes: Principles, Applications, and Problems (J. Rossi). Index.

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Book SynopsisThis book provides the basics by which researchers can approach basic and applied problems of management and enquiry of biosequence databases, as well as learn to develop computer models for the description of biological processes.Trade Review"…an excellent addition to the field of comparative genomics." (ASM News, December 2004) “...will provide interesting reading and perspective to almost everyone involved in biological sciences.” (Quarterly Review of Biology, March 2004) "...certainly deserves a place in institutional libraries...depth of material covered is right for the busy scientist...precise and detailed..." (Briefings in Functional Genomics & Proteomics, Vol 2(4), February 2004) "...this book gives an illuminating look at the study of genomes through their sequence..." (Human Genomics, January 2004) "...covers basic and applied problems in the emerging field of comparative functional genomics..." (Genetic Engineering News, Vol 23(14), 2003)Table of ContentsSECTION I: GENOME FEATURES. Preface. The Prokaryotic Genome. Eukaryotes. Organelles. SECTION II: METHODOLOGIES. Molecular Biology Techniques for Genomics. Biological Databases. Computatonal Methods for the Analysis of Genome Sequence Data. SECTION III: COMPARATIVE GENOMICS. Molecular Evolution. Molecular Phylogeny. Appendix I.

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Book SynopsisTrade ReviewThe DNA Mystique is a wake-up call to all who would dismiss America's love affair with 'the gene' as a merely eccentric obsession." —In These Times"Nelkin and Lindee are to be warmly congratulated for opening up this intriguing field [of genetics in popular culture] to further study." —Nature

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Book SynopsisThe genetic revolution has provided incredibly valuable information about our DNA, information that can be used to benefit and inform - but also to judge, discriminate, and abuse. This book gives the background information critical to understanding how genetics is affecting our everyday lives.Trade Review"Goes a long way toward exploring these issues in a painstaking yet readable scholarly treatise... Throughout, they make a convincing case that we are not our genome alone." New England Journal Of Medicine "The book is anything but dry reporting... An enjoyable and stimulating read for specialists in the field and the curious public alike." Science (AAAS) "Readers will come away with a realistic and encouraging perspective on medical genetics, providing that neither fantastic optimism nor abject fear is necessary to make the true story of DNA exciting." Qtly Review Of Biology "Readers will come away with a realistic and encouraging perspective on medical genetics, proving that neither fantastic optimism nor abject fear is necessary to make the true story of DNA exciting." Qtly Review Of BiologyTable of ContentsForeword by Victor A. McKusick Acknowledgments 1. DNA Sequence Does Not Equal Destiny 2. What Is Genomics? 3. Genetic Determinism 4. The Evolution and Deconstruction of Human-Centered Biology 5. Race and Ethnicity: Your History Is Written in Your Genes 6. Gender as a Spectrum, Not a Dichotomy 7. Genome-Based Forensics 8. When Genes Belong to Groups and Not Individuals 9. Genes as Commodities: Ownership of Genes by Business Interests 10. Protection against Genetic Discrimination: The New Civil Right 11. Reproductive Technologies: On the Road to Designer Babies? 12. Reproductive Cloning: From Farm Animals to Pets to Humans? 13. Therapeutic Cloning and Regenerative Medicine 14. Gene Therapy: Can the Promise Be Fulfilled? 15. Large Population Assessments: The Foundation for Genomic Medicine 16. Hidden Destiny: Unbounded by Your DNA Bibliography Illustration Credits Index

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Book SynopsisThe complete mapping of the horse genome sequence makes a significant contribution to understanding equine biology. This book provides a timely comprehensive overview of equine genomic research.Trade Review“Equine Genomicsis an excellent text that compiles historical accomplishments in equine genetics and molecular biology, describes state-of-theart approaches to understanding the equine genome, and provides glimpses of where the field may go in the future . . . It is our responsibility to try to keep up, and I believe this book will help us do that.” (Journal of the American Veterinary Medical Association, 15 June 2014)Table of ContentsContributors ix Preface xi Chapter 1 Defining the equine genome: The nuclear genome and the mitochondrial genome 1Bhanu P. Chowdhary Chapter 2 Genetic linkage maps 11June Swinburne and Gabriella Lindgren Chapter 3 Physical and comparative maps 49Terje Raudsepp and Bhanu P. Chowdhary Chapter 4 The Y-chromosome 73Terje Raudsepp, Nandina Paria, and Bhanu P. Chowdhary Chapter 5 Unexpected structural features of the equine major histocompatibility complex 93Loren C. Skow and Candice L. Brinkmeyer-Langford Chapter 6 Assembly and analysis of the equine genome sequence 103Claire M. Wade Chapter 7 Genomic tools and resources: Development and applications of an equine SNP genotyping array 113Molly McCue and Jim Mickelson Chapter 8 Functional genomics 125Stephen J. Coleman, Michael J. Mienaltowski, and James N. MacLeod Chapter 9 Coat color genomics 143Samantha A. Brooks and Rebecca R. Bellone Chapter 10 Genomics of skin disorders 155Amy E. Young, Stephen D. White, and Danika L. Bannasvch Chapter 11 Genomics of muscle disorders 171James R. Mickelson, Stephanie J. Valberg, Carrie J. Finno, and Molly E. McCue Chapter 12 Genomics of skeletal disorders 187Ottmar Distl Chapter 13 Genomics of reproduction and fertility 199Terje Raudsepp, Pranab J. Das and Bhanu P. Chowdhary Chapter 14 Genetics of equine neurologic disease 217Carrie J. Finno and Monica Aleman Chapter 15 Molecular genetic testing and karyotyping in the horse 241M. C. T. Penedo and Terje Raudsepp Chapter 16 Genomics of laminitis 255Jim K. Belknap Chapter 17 Genomics of performance 265Emmeline W. Hill, Lisa M. Katz, and David E. MacHugh Chapter 18 Genomics of the circadian clock 285Barbara A. Murphy Chapter 19 Mitochondrial genome: Clues about the evolution of extant equids and genomic diversity of horse breeds 311Cynthia C. Steiner, Kateryna D. Makova, and Oliver A. Ryder Index 323

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Book SynopsisThis volume provides computational methods and reviews various aspects of computational studies of protein aggregation. Chapters discuss the relationship between protein misfolding and protein aggregation, methods of prediction of aggregation propensities of protein, peptides, protein structure, results of computer simulations of aggregation, and computational simulations focused on specific diseases such as Alzheimer''s, Parkinson''s, and preeclampsia. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computer Simulations of Aggregation of Proteins and Peptides aims to ensure successful results in the further study of this vital field.Table of Contents1. Bioinformatics Methods in Predicting Amyloid Propensity of Peptides and Proteins Małgorzata Kotulska and Jakub W. Wojciechowski 2. Protocols for Rational Design of Protein Solubility and Aggregation Properties using Aggrescan3D Standalone Aleksander Kuriata, Aleksandra E. Badaczewska-Dawid, Jordi Pujols, Salvador Ventura, and Sebastian Kmiecik 3. Using Surface Hydrophobicity Together with Empirical Potentials to Identify Protein-Protein Binding Sites. Application to the Interactions of E-cadherins Robert L. Jernigan, Pranav Khade, Ambuj Kumar, and Andrzej Kloczkowski 4. Computational Models for Study of Protein Aggregation Nguyen Truong Co, Mai Suan Li, and Pawel Krupa 5. Probing Protein Aggregation Using the Coarse-Grained UNRES Force Field Ana V. Rojas, Gia G. Maisuradze, Harold A. Scheraga, and Adam Liwo 6. Contact-based Analysis of Aggregation of Intrinsically Disordered Proteins Marek Cieplak, Łukasz Mioduszewski, and Mateusz Chwastyk 7. Molecular Insights into the Effect of Metals on Amyloid Aggregation Yifat Miller 8. From Quantum Mechanics, Classical Mechanics and Bioinformatics to Artificial Intelligence Studies in Neurodegenerative Diseases Orkid Coskuner-Weber, M. Gokhan Habiboglu, David Teplow, and Vladimir N. Uversky 9. Computer Simulations Aimed at Exploring Protein Aggregation and Dissociation Phuong H. Nguyen and Philippe Derreumaux 10. All-atom Molecular Dynamics Simulation Methods for Aggregation of Protein and Peptides: Replica-exchange/permutation and Nonequilibrium Simulations Satoru G. Itoh and Hisashi Okumura 11. Key Factors Controlling Fibril Formation of Proteins Tran Thi Minh Thu, Andrzej Kloczkowski, Mai Suan Li, and Maksim Kouza 12. Determination of the Most Stable Packing Of Peptides From Ribosomal S1 Protein, Protein Bgl2p and Aβ peptide in β-layers during Molecular Dynamics Simulations Glyakina A.V., Balabaev N.K., and Galzitskaya O.V 13. Molecular Dynamics Simulations Of Protein Aggregation: Protocols For Simulation Setup and Analysis with Markov State Models And Transition Networks Suman Samantray, Wibke Schumann, Alexander-Maurice Illig, Arghadwip Paul, Bogdan Barz, and Birgit Strodel 14. Challenges in Experimental Methods Marlena E. Gąsior-Głogowska, Natalia Szulc, and Monika Szefczyk 15. Aggregates Sealed By Ions Giovanni La Penna and Silvia Morante 16. Modifying Amyloid Motif Aggregation through Local Structure Sofia Bali and Lukasz A. Joachimiak 17. Assessing the Stability Of Biological Fibrils By Molecular-Scale Simulations Rodrigo A. Moreira 18. Predictive Modeling of Neurotoxic α-Synuclein Polymorphs Liang Xu, Shayon Bhattacharya, and Damien Thompson 19. Characterization of Amyloidogenic Peptide Aggregability in Helical Subspace Shayon Bhattacharya, Liang Xu, and Damien Thompson 20. Exploration of Protein Aggregations in Parkinson’s Disease through Computational Approaches and Big Data Analytics Saba Shahzadi, Muhammad Yasir, Bisma Jawad, Sumbal Baber, Mubashir Hassan 21. Computational Studies of Protein Aggregation In Preeclampsia Maksim Kouza, Andrzej Kolinski, Irina Buhimschi, and Andrzej Kloczkowski 22. Final remarks Mai Suan Li, Andrzej Kloczkowski, Marek Cieplak, and Maksim Kouza

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Book SynopsisThis volume explores the latest technological advances and covers all facets of systems medicine with respect to precision medicine. The chapters in this book are organized into four parts. Part One highlights the recent achievements in proteomics for biomarkers identification, integration of omics and phenotypic data for precision medicine, and medicine-guided treatment of drug-induced Stevens-Johnson syndrome. Part Two covers systems-based computational approaches for pharmaceutical research and drug development, the principle of optimizing systemic exposure of drugs, and Animal Rule for drug repurposing. Part Three looks at computational tools and methodologies of network biology, quantitative systems toxicology, and modeling and stimulating patient response variabilities. Part Four talks about how systems medicine can address unmet medical and health needs, and identify educational needs. Written in the highly successful Methods in Molecular Biology series format, chapters include Table of ContentsAcknowledgements…Preface…Table of Contents…Contributing Authors…Part I Scientific and Medical Advances1. Mass Spectrometry-Based Proteomics for Biomarker DiscoveryZhijun Cao and Li-Rong Yu2. Integration of Omics and Phenotypic Data for Precision MedicineJuan Zhao, QiPing Feng, and Wei-Qi Wei3. Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis in the Era of Systems MedicineChun-Bing Chen, Chuang-Wei Wang, and Wen-Hung ChungPart II Acceleration of Pharmaceutical Research and Development4. Integration of Engineered Delivery with the Pharmacokinetics of Medical Candidates via Physiology-Based PharmacokineticsYuching Yang and Xinyuan Zhang5. Applications of Quantitative System Pharmacology Modeling to Model Informed Drug DevelopmentAndy Z.X. Zhu and Mark Rogge6. Combating Viral Diseases in the Era of Systems MedicineJane P.F. Bai and Ellen Y. Guo7. Toxicity Analysis of Pentachlorophenol Data with a Bioinformatics Tool SetNatalia Polouliakh, Takeshi Hase, Samik Ghosh, and Hiroaki KitanoPart III Tools and Methodologies8. Virtual Populations for Quantitative Systems Pharmacology ModelsYougan Cheng, Ronny Straube, Abed E. Alnaif, Lu Huang, Tarek A. Leil, and Brian J. Schmidt9. Quantitative Systems Toxicology and Drug Development: The DILIsym ExperiencePaul B. Watkins10. Introduction to Genomic Network Reconstruction for Cancer ResearchGuillermo de Anda Jáuregui, Hugo Tovar, Segio Alcalá-Corona, Enrique Hernández-Lemus11. Learning in Medicine: The Importance of Statistical ThinkingMassimiliano Russo and Bruno Scarpa12. Development and Applications of Interoperable Biomedical Ontologies for Integrative Data and Knowledge Representation and Multiscale Modeling in Systems MedicineYongqun HePart IV Systems Medicine to Address Unmet Medical Needs13. Systems Biology to Address Unmet Medical Needs in Neurological DisordersMasha G. Savelieff, Mohamed H. Noureldein, and Eva L. Feldman14. Informatics in Medical Product Regulation: The Right Drug at the Right Dose for the Right PatientEileen Navarro Almario, Anna Kettermann, and Vaishali Popat15. Personal Dense Dynamic Data Clouds Connect Systems Bio-Medicine to Scientific WellnessGilbert S. Omenn, Andrew T. Magis, Nathan D. Price, and Leroy Hood16. Educational Needs for Quantitative Systems Pharmacology ScientistsJames M. GalloSubject Index List…

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Book SynopsisThe volume presents a small selection of state-of-the-art approaches for studying transposable elements(TE). Chapters guide readers through HTS-based approaches, bioinformatic tools, methods to studyTE protein complexes, and the functional impact on the host. Written in the successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible protocols, and notes on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Transposable Elements: Methods and Protocols aims to be a useful practical guide to researches to help further their study in this field. Table of Contents1. An overview of best practices for transposable element identification, classification and annotation in eukaryotic genomes Fernando Rodriguez and Irina R. Arkhipova 2. Assembly-free annotation and quantification of transposable elements with dnaPipeTE Clément Goubert 3. Best practice for the identification of horizontally transferred transposons James D Galbraith, Zhipeng Qu, Atma M Ivancevic, David L Adelson 4. Genotyping of transposable elements insertions segregating in the human populations using short-read re-alignments Xun Chen, Guillaume Bourque, and Clément Goubert 5. A Pangenome approach to detect and genotype TE insertion polymorphisms Cristian Groza, Guillaume Bourque, and Clément Goubert 6. Experimental validation of transposable element insertions using the Polymerase Chain Reaction (PCR) Miriam Merenciano, Marta Coronado-Zamora, and Josefa González 7. Quantification of LINE-1 RNA expression from bulk RNA-seq using L1EM Wilson McKerrow 8. Genome-wide profiling of L1 DNA methylation by bs-ATLAS-seq Claude Philippe and Gael Cristofari 9. Nanopore epigenomic analysis of transposable element DNA modifications Nathan Smits and Geoffrey Faulkner 10. Targeted resequencing and methylation analysis of L1 elements by nanopore sequencing Arpita Sarkar, Sophie Lanciano, and Gael Cristofari 11. Inferring protein-DNA binding profiles at interspersed repeats using HiChIP and PatChER Darren Taylor and Miguel R Branco 12. Affinity-based Interactome Analysis of Endogenous LINE-1 Macromolecules Luciano H. Di Stefano, Leila Saba, Mehrnoosh Oghbaie, Hua Jiang, Wilson McKerrow, Maria Benitez-Guijarro, Martin S. Taylor, and John LaCava 13. LINE-1 retrotransposition assays in embryonic stem cells Marta Garcia-Canadas, Francisco Sanchez-Luque, Laura Sanchez, Johana Rojas, and Jose Garcia Perez 14. Detecting somatic transposable element insertions in Drosophila tissues Katarzyna Siudeja 15. Precise and scarless insertion of transposable elements by Cas9-mediated genome engineering” Vivien M Weber, Aurelien J Doucet, and Gael Cristofari 16. Epigenetic editing of transposable and repetitive elements Joanna M Jachowicz 17. Using CRISPR to investigate the regulatory activity of transposable elements David M Simpson, Conor R Kelly, and Edward B Chuong

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Book SynopsisThis volume details state-of-the-art computational methods designed to manage, analyze, and generally leverage epigenomic and epitranscriptomic data. Chapters guide readers through fine-mapping and quantification of modifications, visual analytics, imputation methods, supervised analysis, and integrative approaches for single-cell data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Computational Epigenomics and Epitranscriptomics aims to provide an overview of epiomic protocols, making it easier for researchers to extract impactful biological insight from their data.Table of Contents1. DNA methylation data analysis using Msuite Xiaojian Liu, Pengxiang Yuan, and Kun Sun 2. Interactive DNA methylation arrays analysis with ShinyÉPICo Octavio Morante-Palacios 3. Predicting Chromatin Interactions from DNA Sequence using DeepC Ron Schwessinger 4. Integrating single-cell methylome and transcriptome data with MAPLE Yasin Uzun, Hao Wu, and Kai Tan 5. Quantitative comparison of multiple chromatin immunoprecipitation-sequencing (ChIP-seq) experiments with spikChIP Enrique Blanco, Cecilia Ballaré, Luciano Di Croce, and Sergi Aranda 6. A Guide To MethylationToActivity: A Deep-Learning Framework That Reveals Promoter Activity Landscapes from DNA Methylomes In Individual Tumors Karissa Dieseldorff Jones, Daniel Putnam, Justin Williams, and Xiang Chen 7. DNA modification patterns filtering and analysis using DNAModAnnot Alexis Hardy, Sandra Duharcourt, and Matthieu Defrance 8. Methylome imputation by methylation patterns Ya-Ting Chang, Ming-Ren Yen, and Pao-Yang Chen 9. Sequoia: a framework for visual analysis of RNA modifications from direct RNA sequencing data Ratanond Koonchanok, Swapna Vidhur Daulatabad, Khairi Reda, and Sarath Chandra Janga 10. Predicting pseudouridine sites with Porpoise Xudong Guo, Fuyi Li, and Jiangning Song 11. Pseudouridine Identification and Functional Annotation with PIANO Jiahui Yao, Cuiyueyue Hao, Kunqi Chen, Jia Meng, and Bowen Song 12. Analyzing mRNA epigenetic sequencing data with TRESS Zhenxing Guo, Andrew M. Shafik, Peng Jin, Zhijin Wu, and Hao Wu 13. Nanopore Direct RNA Sequencing Data Processing and Analysis Using MasterOfPores Luca Cozzuto, Anna Delgado-Tejedor, Toni Hermoso Pulido, Eva Maria Novoa, and Julia Ponomarenko 14. Data Analysis Pipeline for Detection and Quantification of Pseudouridine (ψ) in RNA by HydraPsiSeq Florian Pichot, Virginie Marchand, Mark Helm, and Yuri Motorin 15. Analysis of RNA sequences and modifications using NASE Samuel Wein 16. Mapping of RNA modifications by direct Nanopore sequencing and JACUSA2 Amina Lemsara, Christoph Dieterich, and Isabel Naarmann-de Vries

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Book SynopsisThis definitive guide shows you how to automate networking for simple and secure application delivery with Consul. Author Luke Kysow demonstrates how this service mesh solution provides a software-driven approach to security, observability, and traffic management.

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Book SynopsisThe simplicity and lack of redundancy in their regulatory genes have made ascidians one of the most useful species in studying developmental genomics. In Developmental Genomics of Ascidians, Dr.Trade Review"In his preface, the author describes Developmental Genomics of Ascidians as his “last and largest contribution to ascidian developmental biology” (p. xi). This book is indeed a major accomplishment and a great resource for the community" (The Quarterly Review of Biology 2016)Table of ContentsPREFACE ix CHAPTER 1 A BRIEF INTRODUCTION TO ASCIDIANS 1 CHAPTER 2 THE DEVELOPMENT OF TADPOLE LARVAE AND SESSILE JUVENILES 9 CHAPTER 3 GENOMICS, TRANSCRIPTOMICS, AND PROTEOMICS 19 CHAPTER 4 RESEARCH TOOLS 31 CHAPTER 5 THE FUNCTION AND REGULATION OF MATERNAL TRANSCRIPTS 41 CHAPTER 6 LARVAL TAIL MUSCLE 53 CHAPTER 7 ENDODERM 63 CHAPTER 8 EPIDERMIS 69 CHAPTER 9 NOTOCHORD 77 CHAPTER 10 THE LARVAL AND ADULT NERVOUS SYSTEMS 89 CHAPTER 11 MESENCHYME 107 CHAPTER 12 MAKING BLUEPRINT OF CHORDATE BODY: DYNAMIC ACTIVITIES OF REGULATORY GENES 113 CHAPTER 13 DEVELOPMENT OF THE JUVENILE HEART 137 CHAPTER 14 GERM-CELL LINE, GAMETES, FERTILIZATION, AND METAMORPHOSIS 145 CHAPTER 15 INNATE IMMUNE SYSTEM AND BLOOD CELLS 159 CHAPTER 16 COLONIAL ASCIDIANS: ASEXUAL REPRODUCTION AND COLONY SPECIFICITY 167 CHAPTER 17 EVOLUTIONARY DEVELOPMENTAL GENOMICS 175 INDEX 193

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Book SynopsisComputational and high-throughput methods, such as genomics, proteomics, and transcriptomics, known collectively as -omics, have been used to study plant biology for well over a decade now. This book provides an introduction to key omicsbased methods and their application in plant breeding.Trade Review“Accessible to advanced students, researchers, and professionals, Omics in Plant Breeding will be an essential entry point into this innovative and exciting field.” (Biotechnology, Agronomy, Society and Environment, 1 October 2014) Table of ContentsList of Contributors ix Foreword xiii 1 Omics: Opening up the "Black Box" of the Phenotype 1Roberto Fritsche-Neto and Aluizio Borem The Post-Genomics Era 3 The Omics in Plant Breeding 4 Genomics, Precision Genomics, and RNA Interference 5 Transcriptomics and Proteomics 8 Metabolomics and Physiognomics 8 Phenomics 9 Bioinformatics 10 Prospects 10 References 10 2 Genomics 13Antonio Costa de Oliveira, Luciano Carlos da Maia, Daniel da Rosa Farias, and Naciele Marini The Rise of Genomics 13 DNA Sequencing 13 Development of Sequence-based Markers 18 Genome Wide Selection (GWS) 25 Structural and Comparative Genomics 27 References 28 3 Transcriptomics 33Carolina Munari Rodrigues, Valeria S. Mafra, and Marcos Antonio Machado Methods of Studying the Transcriptome 34 Applications of Transcriptomics Approaches for Crop Breeding 46 Conclusions and Future Prospects 51 Acknowledgements 51 References 51 4 Proteomics 59Ilara Gabriela F. Budzinski, Thais Regiani, Monica T. Veneziano Labate, Simone Guidetti-Gonzalez, Danielle Izilda R. da Silva, Maria Juliana Calderan Rodrigues, Janaina de Santana Borges, Ivan Miletovic Mozol, and Carlos Alberto Labate History 59 Different Methods for the Extraction of Total Proteins 60 Subcellular Proteomics 64 Post-Translational Modifications 66 Quantitative Proteomics 69 Perspectives 72 References 73 5 Metabolomics 81Valdir Diola (in memoriam), Danilo de Menezes Daloso, and Werner Camargos Antunes Introduction 81 Metabolomic and Biochemical Molecules 83 Technologies for Metabolomics 83 Metabolomic Database Analysis 86 Metabolomics Applications 89 Metabolomics-assisted Plant Breeding 91 Associative Genome Mapping and mQTL Profiles 95 Large-scale Phenotyping Using Metabolomics 97 Conclusion and Outlook 98 References 99 6 Physionomics 103Frederico Almeida de Jesus, Agustin Zsogon, and Lazaro Eustaquio Pereira Peres Introduction 103 Early Studies on Plant Physiology and the Discovery of Photosynthesis 104 Biochemical Approaches to Plant Physiology and the Discovery of Plant Hormones 104 Genetic Approaches to Plant Physiology and the Discovery of Hormone Signal Transduction Pathways 106 Alternative Genetic Models for Omics Approaches in Plant Physiology 112 "Physionomics" as an Integrator of Various Omics for Functional Studies and Plant Breeding 117 Acknowledgements 121 References 121 7 Phenomics 127Roberto Fritsche-Neto, Aluizio Borem, and Joshua N. Cobb Introduction 127 Examples of Large-scale Phenotyping 128 Important Aspects for Phenomics Implementation 134 Main Breeding Applications 141 Final Considerations 144 References 144 8 Electrophoresis, Chromatography, and Mass Spectrometry 147Thais Regiani, Ilara Gabriela F. Budzinski, Simone Guidetti-Gonzalez, Monica T. Veneziano Labate, Fernando Cotinguiba, Felipe G. Marques, Fabricio E. Moraes, and Carlos Alberto Labate Introduction 147 Two-dimensional Electrophoresis (2DE) 148 Chromatography 150 Mass Spectrometry 155 Data Analysis 161 References 164 9 Bioinformatics 167J. Miguel Ortega and Fabricio R. Santos Introduction 167 The "Omics" Megadata and Bioinformatics 167 Hardware for Modern Bioinformatics 169 Software for Genomic Sequencing 170 Software for Contig Assembling 172 Assembly Using the Graph Theory 173 New Approaches in Bioinformatics for DNA and RNA Sequencing 174 Databases, Identification of Homologous Sequences and Functional Annotation 175 Annotation of a Complete Genome 179 Computational System with Chained Tasks Manager (Workflow) 181 Applications for Studies in Plants 182 Final Considerations 183 References 184 10 Precision Genetic Engineering 187Thiago J. Nakayama, Aluizio Borem, Lucimara Chiari, Hugo Bruno Correa Molinari, and Alexandre Lima Nepomuceno Introduction 187 Zinc Finger Nucleases (ZFNs) 190 Transcription Activator-like Effector Nucleases (TALENs) 193 Meganucleases (LHEs: LAGLIDADG Homing Endonucleases) 194 Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) 195 Implications and Perspectives of the use of PGE in Plant Breeding 197 References 202 11 RNA Interference 207Francisco J.L. Aragao, Abdulrazak B. Ibrahim, and Maria Laine P. Tinoco Introduction 207 Discovery of RNAi 208 Mechanism of RNA Interference 209 Applications in Plant Breeding: Naturally Occurring Gene Silencing and Modification by Genetic Engineering 211 Resistance to Viruses 215 Host-induced Gene Silencing 218 Insect and Disease Control 218 Improving Nutritional Values 219 Secondary Metabolites 220 Perspectives 220 References 222 Index 229

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Book SynopsisA thought-provoking exploration of deleterious mutations in the human genome and their effects on human health and wellbeing Despite all of the elaborate mechanisms that a cell employs to handle its DNA with the utmost care, a newborn human carries about 100 new mutations, originated in their parents, about 10 of which are deleterious. A mutation replacing just one of the more than three billion nucleotides in the human genome may lead to synthesis of a dysfunctional protein, and this can be inconsistent with life or cause a tragic disease. Several percent of even young people suffer from diseases that are caused, exclusively or primarily, by pre]existing and new mutations in their genomes, including both a wide variety of genetically simple Mendelian diseases and diverse complex diseases such as birth anomalies, diabetes, and schizophrenia. Milder, but still substantial, negative effects of mutations are even more pervasive. As of now, we possess no means of reducing Table of ContentsPreface ix 1 Genotypes and Phenotypes 1 1.1 DNA is a Text 1 1.2 Genomes Small and Large 6 1.3 Genes and Intergenic Regions 7 1.4 Cells, Mitosis, and Meiosis 14 1.5 From Genotype to Phenotype 17 Further Reading 21 2 Mendelian Inheritance and Population Genetics 23 2.1 Inheritance is Discrete 23 2.2 Populations are Genetically Variable 27 2.3 Loci and Genes 33 2.4 Effects of Alleles on Phenotypes 37 2.5 Mendelian Traits and Diseases 43 Further Reading 46 3 Complex Traits and Their Inheritance 49 3.1 Complex Inheritance of Phenotypes 49 3.2 Properties of a Complex Trait 52 3.3 Complex Traits in Populations 55 3.4 Effects of Heredity and Environment on Complex Traits 60 3.5 Polymorphic Loci Behind Complex Variation 64 Further Reading 68 4 Unavoidable Mutation 71 4.1 Phenomenon of Mutation 71 4.2 Kinds of Mutations 73 4.3 Spontaneous Mutation 75 4.4 Evolution of Mutation Rates 77 4.5 Artificial Mutagenesis and Antimutagenesis 79 Further Reading 81 5 Struggle for Fidelity 83 5.1 Fidelity of DNA Replication 83 5.2 Cleaning Up After the Replisome 88 5.3 Dealing with DNA Damages 91 5.4 Harms of Broken Maintenance 96 5.5 Mechanisms of Mutation 100 Further Reading 104 6 Mutation Rates 107 6.1 Measuring Mutation Rates 107 6.2 Data on Mutation Rates 109 6.3 Guilty Older Men 112 6.4 Rates of Phenotypically Drastic Mutations 114 6.5 Mild Mutations and Mutational Pressures 118 Further Reading 121 7 Natural Selection 123 7.1 Vulnerable Adaptations and Their Evolutionary Origin 123 7.2 Two Basic Characteristics of Selection 127 7.3 Measuring Natural Selection 129 7.4 Selection at a Polymorphic Locus 132 7.5 Selection on a Quantitative Trait 135 Further Reading 138 8 Functioning DNA and Junk DNA 141 8.1 Selective Neutrality and Random Drift 141 8.2 Effective Population Size 144 8.3 Junk DNA Provides the Simplest Evidence for Evolution 144 8.4 Finding Functioning Genome Segments 145 8.5 The Genomic Rate of Deleterious Mutations 147 Further Reading 148 9 It Takes All the Running You Can Do 149 9.1 Middle Class Neighborhood for Drosophila 149 9.2 Selection Against Deleterious Alleles 153 9.3 Mutation–Selection Equilibrium 155 9.4 Inbreeding Depression 158 9.5 Dangerous Slightly Deleterious Alleles 160 Further Reading 162 10 Phenomenon of Imperfection 165 10.1 Phenotypic and Genotypic Imperfection 165 10.2 Five Evolutionary Causes of Imperfection 168 10.3 Weakly Perfect Human Genotypes and Phenotypes 171 10.4 Native, Novel, and Optimal Environments 173 10.5 Factors, Exacerbating Mutation Imperfection 175 Further Reading 176 11 Our Imperfect Fitness 177 11.1 Properties of an Allele 177 11.2 Human Derived Alleles 180 11.3 Average Imperfection of a Genotype 186 11.4 Variation Among Genotypes 190 11.5 Selection in Modern Human Populations 192 Further Reading 197 12 Our Imperfect Wellness 199 12.1 Qualitative Characteristics of Wellness 199 12.2 Quantitative Traits 206 12.3 Contributions of Heredity and Environment 208 12.4 Wellness‐impairing Alleles 211 12.5 Genetic Architecture of Wellness 215 Further Reading 217 13 Mutational Pressure on Our Species 219 13.1 Mutational Pressure on Diseases 219 13.2 Mutational Pressure on Quantitative Traits 225 13.3 Possible Increase of the Mutational Pressure 226 13.4 De Novo Mutations and Human Wellness 228 13.5 Optimistic and Pessimistic Scenarios 230 Further Reading 231 14 Ethical Issues 233 14.1 Lessons from History 233 14.2 Modern Practices 237 14.3 Humanist Ethics and the Main Concern 241 14.4 The Main Concern and Ethical Dilemmas 244 14.5 Role of Scientists 247 Further Reading 250 15 What to Do? 253 15.1 Conditionally Beneficial or Unconditionally Deleterious? 253 15.2 Mutationless Utopia: What Could It Be? 257 15.3 Mutationless Utopia: Is It Ever Going to Happen? 261 15.4 What Can I Do Without Germline Genotype Modification? 265 15.5 Prognosis 268 Further Reading 269 Index 271

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Book SynopsisTable of ContentsPreface xv 1 Probabilistic Optimization of Machine Learning Algorithms for Heart Disease Prediction 1Jaspreet Kaur, Bharti Joshi and Rajashree Shedge 1.1 Introduction 2 1.1.1 Scope and Motivation 3 1.2 Literature Review 4 1.2.1 Comparative Analysis 5 1.2.2 Survey Analysis 5 1.3 Tools and Techniques 10 1.3.1 Description of Dataset 11 1.3.2 Machine Learning Algorithm 12 1.3.3 Decision Tree 14 1.3.4 Random Forest 15 1.3.5 Naive Bayes Algorithm 16 1.3.6 K Means Algorithm 18 1.3.7 Ensemble Method 18 1.3.7.1 Bagging 19 1.3.7.2 Boosting 19 1.3.7.3 Stacking 19 1.3.7.4 Majority Vote 19 1.4 Proposed Method 20 1.4.1 Experiment and Analysis 20 1.4.2 Method 22 1.5 Conclusion 25 References 26 2 Cancerous Cells Detection in Lung Organs of Human Body: IoT-Based Healthcare 4.0 Approach 29Rohit Rastogi, D.K. Chaturvedi, Sheelu Sagar, Neeti Tandon and Mukund Rastogi 2.1 Introduction 30 2.1.1 Motivation to the Study 30 2.1.1.1 Problem Statements 31 2.1.1.2 Authors’ Contributions 31 2.1.1.3 Research Manuscript Organization 31 2.1.1.4 Definitions 32 2.1.2 Computer-Aided Diagnosis System (CADe or CADx) 32 2.1.3 Sensors for the Internet of Things 32 2.1.4 Wireless and Wearable Sensors for Health Informatics 33 2.1.5 Remote Human’s Health and Activity Monitoring 33 2.1.6 Decision-Making Systems for Sensor Data 33 2.1.7 Artificial Intelligence and Machine Learning for Health Informatics 34 2.1.8 Health Sensor Data Management 34 2.1.9 Multimodal Data Fusion for Healthcare 35 2.1.10 Heterogeneous Data Fusion and Context-Aware Systems: A Context-Aware Data Fusion Approach for Health-IoT 35 2.2 Literature Review 35 2.3 Proposed Systems 37 2.3.1 Framework or Architecture of the Work 38 2.3.2 Model Steps and Parameters 38 2.3.3 Discussions 39 2.4 Experimental Results and Analysis 39 2.4.1 Tissue Characterization and Risk Stratification 39 2.4.2 Samples of Cancer Data and Analysis 40 2.5 Novelties 42 2.6 Future Scope, Limitations, and Possible Applications 42 2.7 Recommendations and Consideration 43 2.8 Conclusions 43 References 43 3 Computational Predictors of the Predominant Protein Function: SARS-CoV-2 Case 47Carlos Polanco, Manlio F. Márquez and Gilberto Vargas-Alarcón 3.1 Introduction 48 3.2 Human Coronavirus Types 49 3.3 The SARS-CoV-2 Pandemic Impact 50 3.3.1 RNA Virus vs DNA Virus 51 3.3.2 The Coronaviridae Family 51 3.3.3 The SARS-CoV-2 Structural Proteins 52 3.3.4 Protein Representations 52 3.4 Computational Predictors 53 3.4.1 Supervised Algorithms 53 3.4.2 Non-Supervised Algorithms 54 3.5 Polarity Index Method® 54 3.5.1 The PIM® Profile 54 3.5.2 Advantages 55 3.5.3 Disadvantages 55 3.5.4 SARS-CoV-2 Recognition Using PIM® Profile 55 3.6 Future Implications 59 3.7 Acknowledgments 60 References 60 4 Deep Learning in Gait Abnormality Detection: Principles and Illustrations 63Saikat Chakraborty, Sruti Sambhavi and Anup Nandy 4.1 Introduction 63 4.2 Background 65 4.2.1 LSTM 65 4.2.1.1 Vanilla LSTM 65 4.2.1.2 Bidirectional LSTM 66 4.3 Related Works 67 4.4 Methods 68 4.4.1 Data Collection and Analysis 68 4.4.2 Results and Discussion 69 4.5 Conclusion and Future Work 71 4.6 Acknowledgments 71 References 71 5 Broad Applications of Network Embeddings in Computational Biology, Genomics, Medicine, and Health 73Akanksha Jaiswar, Devender Arora, Manisha Malhotra, Abhimati Shukla and Nivedita Rai 5.1 Introduction 74 5.2 Types of Biological Networks 76 5.3 Methodologies in Network Embedding 76 5.4 Attributed and Non-Attributed Network Embedding 82 5.5 Applications of Network Embedding in Computational Biology 83 5.5.1 Understanding Genomic and Protein Interaction via Network Alignment 83 5.5.2 Pharmacogenomics 84 5.5.2.1 Drug-Target Interaction Prediction 84 5.5.2.2 Drug-Drug Interaction 84 5.5.2.3 Drug-Disease Interaction Prediction 85 5.5.2.4 Analysis of Adverse Drug Reaction 85 5.5.3 Function Prediction 86 5.5.4 Community Detection 86 5.5.5 Network Denoising 87 5.5.6 Analysis of Multi-Omics Data 87 5.6 Limitations of Network Embedding in Biology 87 5.7 Conclusion and Outlook 89 References 89 6 Heart Disease Classification Using Regional Wall Thickness by Ensemble Classifier 99Prakash J., Vinoth Kumar B. and Sandhya R. 6.1 Introduction 100 6.2 Related Study 101 6.3 Methodology 103 6.3.1 Pre-Processing 103 6.3.2 Region of Interest Extraction 104 6.3.3 Segmentation 105 6.3.4 Feature Extraction 106 6.3.5 Disease Classification 107 6.4 Implementation and Result Analysis 108 6.4.1 Dataset Description 108 6.4.2 Testbed 108 6.4.3 Discussion 108 6.4.3.1 K-Fold Cross-Validation 110 6.4.3.2 Confusion Matrix 110 6.5 Conclusion 115 References 115 7 Deep Learning for Medical Informatics and Public Health 117K. Aditya Shastry, Sanjay H. A., Lakshmi M. and Preetham N. 7.1 Introduction 118 7.2 Deep Learning Techniques in Medical Informatics and Public Health 121 7.2.1 Autoencoders 122 7.2.2 Recurrent Neural Network 123 7.2.3 Convolutional Neural Network (CNN) 124 7.2.4 Deep Boltzmann Machine 126 7.2.5 Deep Belief Network 127 7.3 Applications of Deep Learning in Medical Informatics and Public Health 128 7.3.1 The Use of DL for Cancer Diagnosis 128 7.3.2 DL in Disease Prediction and Treatment 129 7.3.3 Future Applications 133 7.4 Open Issues Concerning DL in Medical Informatics and Public Health 135 7.5 Conclusion 139 References 140 8 An Insight Into Human Pose Estimation and Its Applications 147Shambhavi Mishra, Janamejaya Channegowda and Kasina Jyothi Swaroop 8.1 Foundations of Human Pose Estimation 147 8.2 Challenges to Human Pose Estimation 149 8.2.1 Motion Blur 150 8.2.2 Indistinct Background 151 8.2.3 Occlusion or Self-Occlusion 151 8.2.4 Lighting Conditions 151 8.3 Analyzing the Dimensions 152 8.3.1 2D Human Pose Estimation 152 8.3.1.1 Single-Person Pose Estimation 153 8.3.1.2 Multi-Person Pose Estimation 153 8.3.2 3D Human Pose Estimation 153 8.4 Standard Datasets for Human Pose Estimation 154 8.4.1 Pascal VOC (Visual Object Classes) Dataset 156 8.4.2 KTH Multi-View Football Dataset I 156 8.4.3 KTH Multi-View Football Dataset II 156 8.4.4 MPII Human Pose Dataset 157 8.4.5 BBC Pose 157 8.4.6 COCO Dataset 157 8.4.7 J-HMDB Dataset 158 8.4.8 Human3.6M Dataset 158 8.4.9 DensePose 158 8.4.10 AMASS Dataset 159 8.5 Deep Learning Revolutionizing Pose Estimation 159 8.5.1 Approaches in 2D Human Pose Estimation 159 8.5.2 Approaches in 3D Human Pose Estimation 163 8.6 Application of Human Pose Estimation in Medical Domains 165 8.7 Conclusion 166 References 167 9 Brain Tumor Analysis Using Deep Learning: Sensor and IoT-Based Approach for Futuristic Healthcare 171Rohit Rastogi, D.K. Chaturvedi, Sheelu Sagar, Neeti Tandon and Akshit Rajan Rastogi 9.1 Introduction 172 9.1.1 Brain Tumor 172 9.1.2 Big Data Analytics in Health Informatics 172 9.1.3 Machine Learning in Healthcare 173 9.1.4 Sensors for Internet of Things 173 9.1.5 Challenges and Critical Issues of IoT in Healthcare 174 9.1.6 Machine Learning and Artificial Intelligence for Health Informatics 174 9.1.7 Health Sensor Data Management 175 9.1.8 Multimodal Data Fusion for Healthcare 175 9.1.9 Heterogeneous Data Fusion and Context-Aware Systems a Context-Aware Data Fusion Approach for Health-IoT 176 9.1.10 Role of Technology in Addressing the Problem of Integration of Healthcare System 176 9.2 Literature Survey 177 9.3 System Design and Methodology 179 9.3.1 System Design 179 9.3.2 CNN Architecture 180 9.3.3 Block Diagram 181 9.3.4 Algorithm(s) 181 9.3.5 Our Experimental Results, Interpretation, and Discussion 183 9.3.6 Implementation Details 183 9.3.7 Snapshots of Interfaces 184 9.3.8 Performance Evaluation 186 9.3.9 Comparison with Other Algorithms 186 9.4 Novelty in Our Work 186 9.5 Future Scope, Possible Applications, and Limitations 188 9.6 Recommendations and Consideration 188 9.7 Conclusions 188 References 189 10 Study of Emission From Medicinal Woods to Curb Threats of Pollution and Diseases: Global Healthcare Paradigm Shift in 21st Century 191Rohit Rastogi, Mamta Saxena, Devendra Kr. Chaturvedi, Sheelu Sagar, Neha Gupta, Harshit Gupta, Akshit Rajan Rastogi, Divya Sharma, Manu Bhardwaj and Pranav Sharma 10.1 Introduction 192 10.1.1 Scenario of Pollution and Need to Connect with Indian Culture 192 10.1.2 Global Pollution Scenario 192 10.1.3 Indian Crisis on Pollution and Worrying Stats 193 10.1.4 Efforts Made to Curb Pollution World Wide 194 10.1.5 Indian Ancient Vedic Sciences to Curb Pollution and Related Disease 196 10.1.6 The Yajna Science: A Boon to Human Race From Rishi-Muni 196 10.1.7 The Science of Mantra Associated With Yajna and Its Scientific Effects 197 10.1.8 Effect of Different Woods and Cow Dung Used in Yajna 197 10.1.9 Use of Sensors and IoT to Record Experimental Data 198 10.1.10 Analysis and Pattern Recognition by ML and AI 199 10.2 Literature Survey 200 10.3 The Methodology and Protocols Followed 201 10.4 Experimental Setup of an Experiment 202 10.5 Results and Discussions 202 10.5.1 Mango 202 10.5.2 Bargad 203 10.6 Applications of Yagya and Mantra Therapy in Pollution Control and Its Significance 207 10.7 Future Research Perspectives 207 10.8 Novelty of Our Research 208 10.9 Recommendations 208 10.10 Conclusions 209 References 209 11 An Economical Machine Learning Approach for Anomaly Detection in IoT Environment 215Ambika N. 11.1 Introduction 215 11.2 Literature Survey 218 11.3 Proposed Work 229 11.4 Analysis of the Work 230 11.5 Conclusion 231 References 231 12 Indian Science of Yajna and Mantra to Cure Different Diseases: An Analysis Amidst Pandemic With a Simulated Approach 235Rohit Rastogi, Mamta Saxena, Devendra Kumar Chaturvedi, Mayank Gupta, Puru Jain, Rishabh Jain, Mohit Jain, Vishal Sharma, Utkarsh Sangam, Parul Singhal and Priyanshi Garg 12.1 Introduction 236 12.1.1 Different Types of Diseases 236 12.1.1.1 Diabetes (Madhumeha) and Its Types 236 12.1.1.2 TTH and Stress 237 12.1.1.3 Anxiety 237 12.1.1.4 Hypertension 237 12.1.2 Machine Vision 237 12.1.2.1 Medical Images and Analysis 238 12.1.2.2 Machine Learning in Healthcare 238 12.1.2.3 Artificial Intelligence in Healthcare 239 12.1.3 Big Data and Internet of Things (IoT) 239 12.1.4 Machine Learning in Association with Data Science and Analytics 239 12.1.5 Yajna Science 240 12.1.6 Mantra Science 240 12.1.6.1 Positive Impact of Recital of Gayatri Mantra and OM Chanting 241 12.1.6.2 Significance of Mantra on Indian Culture and Mythology 241 12.1.7 Usefulness and Positive Aspect of Yoga Asanas and Pranayama 241 12.1.8 Effects of Yajna and Mantra on Human Health 242 12.1.9 Impact of Yajna in Reducing the Atmospheric Solution 242 12.1.10 Scientific Study on Impact of Yajna on Air Purification 243 12.1.11 Scientific Meaning of Religious and Manglik Signs 244 12.2 Literature Survey 244 12.3 Methodology 246 12.4 Results and Discussion 249 12.5 Interpretations and Analysis 250 12.6 Novelty in Our Work 258 12.7 Recommendations 259 12.8 Future Scope and Possible Applications 260 12.9 Limitations 261 12.10 Conclusions 261 12.11 Acknowledgments 262 References 262 13 Collection and Analysis of Big Data From Emerging Technologies in Healthcare 269Nagashri K., Jayalakshmi D. S. and Geetha J. 13.1 Introduction 269 13.2 Data Collection 271 13.2.1 Emerging Technologies in Healthcare and Its Applications 271 13.2.1.1 RFID 272 13.2.1.2 WSN 273 13.2.1.3 IoT 274 13.2.2 Issues and Challenges in Data Collection 277 13.2.2.1 Data Quality 277 13.2.2.2 Data Quantity 277 13.2.2.3 Data Access 278 13.2.2.4 Data Provenance 278 13.2.2.5 Security 278 13.2.2.6 Other Challenges 279 13.3 Data Analysis 280 13.3.1 Data Analysis Approaches 280 13.3.1.1 Machine Learning 280 13.3.1.2 Deep Learning 281 13.3.1.3 Natural Language Processing 281 13.3.1.4 High-Performance Computing 281 13.3.1.5 Edge-Fog Computing 282 13.3.1.6 Real-Time Analytics 282 13.3.1.7 End-User Driven Analytics 282 13.3.1.8 Knowledge-Based Analytics 283 13.3.2 Issues and Challenges in Data Analysis 283 13.3.2.1 Multi-Modal Data 283 13.3.2.2 Complex Domain Knowledge 283 13.3.2.3 Highly Competent End-Users 283 13.3.2.4 Supporting Complex Decisions 283 13.3.2.5 Privacy 284 13.3.2.6 Other Challenges 284 13.4 Research Trends 284 13.5 Conclusion 286 References 286 14 A Complete Overview of Sign Language Recognition and Translation Systems 289Kasina Jyothi Swaroop, Janamejaya Channegowda and Shambhavi Mishra 14.1 Introduction 289 14.2 Sign Language Recognition 290 14.2.1 Fundamentals of Sign Language Recognition 290 14.2.2 Requirements for the Sign Language Recognition 292 14.3 Dataset Creation 293 14.3.1 American Sign Language 293 14.3.2 German Sign Language 296 14.3.3 Arabic Sign Language 297 14.3.4 Indian Sign Language 298 14.4 Hardware Employed for Sign Language Recognition 299 14.4.1 Glove/Sensor-Based Systems 299 14.4.2 Microsoft Kinect–Based Systems 300 14.5 Computer Vision–Based Sign Language Recognition and Translation Systems 302 14.5.1 Image Processing Techniques for Sign Language Recognition 302 14.5.2 Deep Learning Methods for Sign Language Recognition 304 14.5.3 Pose Estimation Application to Sign Language Recognition 305 14.5.4 Temporal Information in Sign Language Recognition and Translation 306 14.6 Sign Language Translation System—A Brief Overview 307 14.7 Conclusion 309 References 310 Index 315

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Book SynopsisTable of ContentsPreface xv Part I: Introduction 1 1 Machine Learning and Big Data: An Approach Toward Better Healthcare Services 3Nahid Sami and Asfia Aziz 1.1 Introduction 3 1.2 Machine Learning in Healthcare 4 1.3 Machine Learning Algorithms 6 1.3.1 Supervised Learning 6 1.3.2 Unsupervised Learning 7 1.3.3 Semi-Supervised Learning 7 1.3.4 Reinforcement Learning 8 1.3.5 Deep Learning 8 1.4 Big Data in Healthcare 8 1.5 Application of Big Data in Healthcare 9 1.5.1 Electronic Health Records 9 1.5.2 Helping in Diagnostics 9 1.5.3 Preventive Medicine 10 1.5.4 Precision Medicine 10 1.5.5 Medical Research 10 1.5.6 Cost Reduction 10 1.5.7 Population Health 10 1.5.8 Telemedicine 10 1.5.9 Equipment Maintenance 11 1.5.10 Improved Operational Efficiency 11 1.5.11 Outbreak Prediction 11 1.6 Challenges for Big Data 11 1.7 Conclusion 11 References 12 Part II: Medical Data Processing and Analysis 15 2 Thoracic Image Analysis Using Deep Learning 17Rakhi Wajgi, Jitendra V. Tembhurne and Dipak Wajgi 2.1 Introduction 18 2.2 Broad Overview of Research 19 2.2.1 Challenges 19 2.2.2 Performance Measuring Parameters 21 2.2.3 Availability of Datasets 21 2.3 Existing Models 23 2.4 Comparison of Existing Models 30 2.5 Summary 38 2.6 Conclusion and Future Scope 38 References 39 3 Feature Selection and Machine Learning Models for High-Dimensional Data: State-of-the-Art 43G. Manikandan and S. Abirami 3.1 Introduction 43 3.1.1 Motivation of the Dimensionality Reduction 45 3.1.2 Feature Selection and Feature Extraction 46 3.1.3 Objectives of the Feature Selection 47 3.1.4 Feature Selection Process 47 3.2 Types of Feature Selection 48 3.2.1 Filter Methods 49 3.2.1.1 Correlation-Based Feature Selection 49 3.2.1.2 The Fast Correlation-Based Filter 50 3.2.1.3 The INTERACT Algorithm 51 3.2.1.4 ReliefF 51 3.2.1.5 Minimum Redundancy Maximum Relevance 52 3.2.2 Wrapper Methods 52 3.2.3 Embedded Methods 53 3.2.4 Hybrid Methods 54 3.3 Machine Learning and Deep Learning Models 55 3.3.1 Restricted Boltzmann Machine 55 3.3.2 Autoencoder 56 3.3.3 Convolutional Neural Networks 57 3.3.4 Recurrent Neural Network 58 3.4 Real-World Applications and Scenario of Feature Selection 58 3.4.1 Microarray 58 3.4.2 Intrusion Detection 59 3.4.3 Text Categorization 59 3.5 Conclusion 59 References 60 4 A Smart Web Application for Symptom-Based Disease Detection and Prediction Using State-of-the-Art ML and ANN Models 65Parvej Reja Saleh and Eeshankur Saikia 4.1 Introduction 65 4.2 Literature Review 68 4.3 Dataset, EDA, and Data Processing 69 4.4 Machine Learning Algorithms 72 4.4.1 Multinomial Naïve Bayes Classifier 72 4.4.2 Support Vector Machine Classifier 72 4.4.3 Random Forest Classifier 73 4.4.4 K-Nearest Neighbor Classifier 74 4.4.5 Decision Tree Classifier 74 4.4.6 Logistic Regression Classifier 75 4.4.7 Multilayer Perceptron Classifier 76 4.5 Work Architecture 77 4.6 Conclusion 78 References 79 5 Classification of Heart Sound Signals Using Time-Frequency Image Texture Features 81Sujata Vyas, Mukesh D. Patil and Gajanan K. Birajdar 5.1 Introduction 81 5.1.1 Motivation 82 5.2 Related Work 83 5.3 Theoretical Background 84 5.3.1 Pre-Processing Techniques 84 5.3.2 Spectrogram Generation 85 5.3.2 Feature Extraction 88 5.3.4 Feature Selection 90 5.3.5 Support Vector Machine 91 5.4 Proposed Algorithm 91 5.5 Experimental Results 92 5.5.1 Database 92 5.5.2 Evaluation Metrics 94 5.5.3 Confusion Matrix 94 5.5.4 Results and Discussions 94 5.6 Conclusion 96 References 99 6 Improving Multi-Label Classification in Prototype Selection Scenario 103Himanshu Suyal and Avtar Singh 6.1 Introduction 103 6.2 Related Work 105 6.3 Methodology 106 6.3.1 Experiments and Evaluation 108 6.4 Performance Evaluation 108 6.5 Experiment Data Set 109 6.6 Experiment Results 110 6.7 Conclusion 117 References 117 7 A Machine Learning–Based Intelligent Computational Framework for the Prediction of Diabetes Disease 121Maqsood Hayat, Yar Muhammad and Muhammad Tahir 7.1 Introduction 121 7.2 Materials and Methods 123 7.2.1 Dataset 123 7.2.2 Proposed Framework for Diabetes System 124 7.2.3 Pre-Processing of Data 124 7.3 Machine Learning Classification Hypotheses 124 7.3.1 K-Nearest Neighbor 124 7.3.2 Decision Tree 125 7.3.3 Random Forest 126 7.3.4 Logistic Regression 126 7.3.5 Naïve Bayes 126 7.3.6 Support Vector Machine 126 7.3.7 Adaptive Boosting 126 7.3.8 Extra-Tree Classifier 127 7.4 Classifier Validation Method 127 7.4.1 K-Fold Cross-Validation Technique 127 7.5 Performance Evaluation Metrics 127 7.6 Results and Discussion 129 7.6.1 Performance of All Classifiers Using 5-Fold CV Method 129 7.6.2 Performance of All Classifiers Using the 7-Fold Cross-Validation Method 131 7.6.3 Performance of All Classifiers Using 10-Fold CV Method 133 7.7 Conclusion 137 References 137 8 Hyperparameter Tuning of Ensemble Classifiers Using Grid Search and Random Search for Prediction of Heart Disease 139Dhilsath Fathima M. and S. Justin Samuel 8.1 Introduction 140 8.2 Related Work 140 8.3 Proposed Method 142 8.3.1 Dataset Description 143 8.3.2 Ensemble Learners for Classification Modeling 144 8.3.2.1 Bagging Ensemble Learners 145 8.3.2.2 Boosting Ensemble Learner 147 8.3.3 Hyperparameter Tuning of Ensemble Learners 151 8.3.3.1 Grid Search Algorithm 151 8.3.3.2 Random Search Algorithm 152 8.4 Experimental Outcomes and Analyses 153 8.4.1 Characteristics of UCI Heart Disease Dataset 153 8.4.2 Experimental Result of Ensemble Learners and Performance Comparison 154 8.4.3 Analysis of Experimental Result 154 8.5 Conclusion 157 References 157 9 Computational Intelligence and Healthcare Informatics Part III—Recent Development and Advanced Methodologies 159Sankar Pariserum Perumal, Ganapathy Sannasi, Santhosh Kumar S.V.N. and Kannan Arputharaj 9.1 Introduction: Simulation in Healthcare 160 9.2 Need for a Healthcare Simulation Process 160 9.3 Types of Healthcare Simulations 161 9.4 AI in Healthcare Simulation 163 9.4.1 Machine Learning Models in Healthcare Simulation 163 9.4.1.1 Machine Learning Model for Post-Surgical Risk Prediction 163 9.4.2 Deep Learning Models in Healthcare Simulation 169 9.4.2.1 Bi-LSTM–Based Surgical Participant Prediction Model 170 9.5 Conclusion 174 References 174 10 Wolfram’s Cellular Automata Model in Health Informatics 179Sutapa Sarkar and Mousumi Saha 10.1 Introduction 179 10.2 Cellular Automata 181 10.3 Application of Cellular Automata in Health Science 183 10.4 Cellular Automata in Health Informatics 184 10.5 Health Informatics–Deep Learning–Cellular Automata 190 10.6 Conclusion 191 References 191 Part III: Machine Learning and COVID Prospective 193 11 COVID-19: Classification of Countries for Analysis and Prediction of Global Novel Corona Virus Infections Disease Using Data Mining Techniques 195Sachin Kamley, Shailesh Jaloree, R.S. Thakur and Kapil Saxena 11.1 Introduction 195 11.2 Literature Review 196 11.3 Data Pre-Processing 197 11.4 Proposed Methodologies 198 11.4.1 Simple Linear Regression 198 11.4.2 Association Rule Mining 202 11.4.3 Back Propagation Neural Network 203 11.5 Experimental Results 204 11.6 Conclusion and Future Scopes 211 References 212 12 Sentiment Analysis on Social Media for Emotional Prediction During COVID-19 Pandemic Using Efficient Machine Learning Approach 215Sivanantham Kalimuthu 12.1 Introduction 215 12.2 Literature Review 218 12.3 System Design 222 12.3.1 Extracting Feature With WMAR 224 12.4 Result and Discussion 229 12.5 Conclusion 232 References 232 13 Primary Healthcare Model for Remote Area Using Self-Organizing Map Network 235Sayan Das and Jaya Sil 13.1 Introduction 236 13.2 Background Details and Literature Review 239 13.2.1 Fuzzy Set 239 13.2.2 Self-Organizing Mapping 239 13.3 Methodology 240 13.3.1 Severity_Factor of Patient 244 13.3.2 Clustering by Self-Organizing Mapping 249 13.4 Results and Discussion 250 13.5 Conclusion 252 References 252 14 Face Mask Detection in Real-Time Video Stream Using Deep Learning 255Alok Negi and Krishan Kumar 14.1 Introduction 256 14.2 Related Work 257 14.3 Proposed Work 258 14.3.1 Dataset Description 258 14.3.2 Data Pre-Processing and Augmentation 258 14.3.3 VGG19 Architecture and Implementation 259 14.3.4 Face Mask Detection From Real-Time Video Stream 261 14.4 Results and Evaluation 262 14.5 Conclusion 267 References 267 15 A Computational Intelligence Approach for Skin Disease Identification Using Machine/Deep Learning Algorithms 269Swathi Jamjala Narayanan, Pranav Raj Jaiswal, Ariyan Chowdhury, Amitha Maria Joseph and Saurabh Ambar 15.1 Introduction 270 15.2 Research Problem Statements 274 15.3 Dataset Description 274 15.4 Machine Learning Technique Used for Skin Disease Identification 276 15.4.1 Logistic Regression 277 15.4.1.1 Logistic Regression Assumption 277 15.4.1.2 Logistic Sigmoid Function 277 15.4.1.3 Cost Function and Gradient Descent 278 15.4.2 SVM 279 15.4.3 Recurrent Neural Networks 281 15.4.4 Decision Tree Classification Algorithm 283 15.4.5 CNN 286 15.4.6 Random Forest 288 15.5 Result and Analysis 290 15.6 Conclusion 291 References 291 16 Asymptotic Patients’ Healthcare Monitoring and Identification of Health Ailments in Post COVID-19 Scenario 297Pushan K.R. Dutta, Akshay Vinayak and Simran Kumari 16.1 Introduction 298 16.1.1 Motivation 298 16.1.2 Contributions 299 16.1.3 Paper Organization 299 16.1.4 System Model Problem Formulation 299 16.1.5 Proposed Methodology 300 16.2 Material Properties and Design Specifications 301 16.2.1 Hardware Components 301 16.2.1.1 Microcontroller 301 16.2.1.2 ESP8266 Wi-Fi Shield 301 16.2.2 Sensors 301 16.2.2.1 Temperature Sensor (LM 35) 301 16.2.2.2 ECG Sensor (AD8232) 301 16.2.2.3 Pulse Sensor 301 16.2.2.4 GPS Module (NEO 6M V2) 302 16.2.2.5 Gyroscope (GY-521) 302 16.2.3 Software Components 302 16.2.3.1 Arduino Software 302 16.2.3.2 MySQL Database 302 16.2.3.3 Wireless Communication 302 16.3 Experimental Methods and Materials 303 16.3.1 Simulation Environment 303 16.3.1.1 System Hardware 303 16.3.1.2 Connection and Circuitry 304 16.3.1.3 Protocols Used 306 16.3.1.4 Libraries Used 307 16.4 Simulation Results 307 16.5 Conclusion 310 16.6 Abbreviations and Acronyms 310 References 311 17 COVID-19 Detection System Using Cellular Automata–Based Segmentation Techniques 313Rupashri Barik, M. Nazma B. J. Naskar and Sarbajyoti Mallik 17.1 Introduction 313 17.2 Literature Survey 314 17.2.1 Cellular Automata 315 17.2.2 Image Segmentation 316 17.2.3 Deep Learning Techniques 316 17.3 Proposed Methodology 317 17.4 Results and Discussion 320 17.5 Conclusion 322 References 322 18 Interesting Patterns From COVID-19 Dataset Using Graph-Based Statistical Analysis for Preventive Measures 325Abhilash C. B. and Kavi Mahesh 18.1 Introduction 326 18.2 Methods 326 18.2.1 Data 326 18.3 GSA Model: Graph-Based Statistical Analysis 327 18.4 Graph-Based Analysis 329 18.4.1 Modeling Your Data as a Graph 329 18.4.2 RDF for Knowledge Graph 331 18.4.3 Knowledge Graph Representation 331 18.4.4 RDF Triple for KaTrace 333 18.4.5 Cipher Query Operation on Knowledge Graph 335 18.4.5.1 Inter-District Travel 335 18.4.5.2 Patient 653 Spread Analysis 336 18.4.5.3 Spread Analysis Using Parent-Child Relationships 337 18.4.5.4 Delhi Congregation Attended the Patient’s Analysis 339 18.5 Machine Learning Techniques 339 18.5.1 Apriori Algorithm 339 18.5.2 Decision Tree Classifier 341 18.5.3 System Generated Facts on Pandas 343 18.5.4 Time Series Model 345 18.6 Exploratory Data Analysis 346 18.6.1 Statistical Inference 347 18.7 Conclusion 356 18.8 Limitations 356 Acknowledgments 356 Abbreviations 357 References 357 Part IV: Prospective of Computational Intelligence in Healthcare 359 19 Conceptualizing Tomorrow’s Healthcare Through Digitization 361Riddhi Chatterjee, Ratula Ray, Satya Ranjan Dash and Om Prakash Jena 19.1 Introduction 361 19.2 Importance of IoMT in Healthcare 362 19.3 Case Study I: An Integrated Telemedicine Platform in Wake of the COVID-19 Crisis 363 19.3.1 Introduction to the Case Study 363 19.3.2 Merits 363 19.3.3 Proposed Design 363 19.3.3.1 Homecare 363 19.3.3.2 Healthcare Provider 365 19.3.3.3 Community 367 19.4 Case Study II: A Smart Sleep Detection System to Track the Sleeping Pattern in Patients Suffering From Sleep Apnea 371 19.4.1 Introduction to the Case Study 371 19.4.2 Proposed Design 373 19.5 Future of Smart Healthcare 375 19.6 Conclusion 375 References 375 20 Domain Adaptation of Parts of Speech Annotators in Hindi Biomedical Corpus: An NLP Approach 377Pitambar Behera and Om Prakash Jena 20.1 Introduction 377 20.1.1 COVID-19 Pandemic Situation 378 20.1.2 Salient Characteristics of Biomedical Corpus 378 20.2 Review of Related Literature 379 20.2.1 Biomedical NLP Research 379 20.2.2 Domain Adaptation 379 20.2.3 POS Tagging in Hindi 380 20.3 Scope and Objectives 380 20.3.1 Research Questions 380 20.3.2 Research Problem 380 20.3.3 Objectives 381 20.4 Methodological Design 381 20.4.1 Method of Data Collection 381 20.4.2 Method of Data Annotation 381 20.4.2.1 The BIS Tagset 381 20.4.2.2 ILCI Semi-Automated Annotation Tool 382 20.4.2.3 IA Agreement 383 20.4.3 Method of Data Analysis 383 20.4.3.1 The Theory of Support Vector Machines 384 20.4.3.2 Experimental Setup 384 20.5 Evaluation 385 20.5.1 Error Analysis 386 20.5.2 Fleiss’ Kappa 388 20.6 Issues 388 20.7 Conclusion and Future Work 388 Acknowledgements 389 References 389 21 Application of Natural Language Processing in Healthcare 393Khushi Roy, Subhra Debdas, Sayantan Kundu, Shalini Chouhan, Shivangi Mohanty and Biswarup Biswas 21.1 Introduction 393 21.2 Evolution of Natural Language Processing 395 21.3 Outline of NLP in Medical Management 396 21.4 Levels of Natural Language Processing in Healthcare 397 21.5 Opportunities and Challenges From a Clinical Perspective 399 21.5.1 Application of Natural Language Processing in the Field of Medical Health Records 399 21.5.2 Using Natural Language Processing for Large-Sample Clinical Research 400 21.6 Openings and Difficulties From a Natural Language Processing Point of View 401 21.6.1 Methods for Developing Shareable Data 401 21.6.2 Intrinsic Evaluation and Representation Levels 402 21.6.3 Beyond Electronic Health Record Data 403 21.7 Actionable Guidance and Directions for the Future 403 21.8 Conclusion 406 References 406 Index 409

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Book SynopsisEdited and written by leading researchers from around the world, The Ecological Genomics of Fungi covers a broad diversity of fungal systems and provides unique insight into the functions of those fungi in various ecosystems, from soil, to plant, to human.Trade Review“I think the volume may succeed in its ambition to serve as a catalyst for further studies by showing researchers venturing into ecological genomics and those already in genomics the width of the field. This may, in turn, further more integrative studies that will benefit our understanding of fungi.” (The Quarterly Review of Biology, 1 October 2015) Table of ContentsContributors vii Preface xiii Section 1 Sequencing Fungal Genomes 1 1 A Changing Landscape of Fungal Genomics 3 Igor V. Grigoriev 2 Repeated Elements in Filamentous Fungi with a Focus on Wood-Decay Fungi 21 Claude Murat, Thibaut Payen, Denis Petitpierre, and Jessy Labbé Section 2 Saprotrophic Fungi 41 3 Wood Decay 43 Dan Cullen 4 Aspergilli and Biomass-Degrading Fungi 63 Isabelle Benoit, Ronald P. de Vries, Scott E. Baker, and Sue A. Karagiosis 5 Ecological Genomics of Trichoderma 89 Irina S. Druzhinina and Christian P. Kubicek Section 3 Plant-Interacting Fungi 117 6 Dothideomycetes: Plant Pathogens, Saprobes, and Extremophiles 119 Stephen B. Goodwin 7 Biotrophic Fungi (Powdery Mildews, Rusts, and Smuts) 149 Sébastien Duplessis, Pietro D. Spanu, and Jan Schirawski 8 The Mycorrhizal Symbiosis Genomics 169 Francis Martin and Annegret Kohler 9 Lichen Genomics: Prospects and Progress 191 Martin Grube, Gabriele Berg, ólafur S. Andrésson, Oddur Vilhelmsson, Paul S. Dyer, and Vivian P.W. Miao Section 4 Animal-Interacting Fungi 213 10 Ecogenomics of Human and Animal Basidiomycetous Yeast Pathogens 215 Sheng Sun, Ferry Hagen, Jun Xu, Tom Dawson, Joseph Heitman, James Kronstad, Charles Saunders, and Teun Boekhout 11 Genomics of Entomopathogenic Fungi 243 Chengshu Wang and Raymond J. St. Leger 12 Ecological Genomics of the Microsporidia 261 Nicolas Corradi and Patrick J. Keeling Section 5 Metagenomics and Biogeography of Fungi 279 13 Metagenomics for Study of Fungal Ecology 281 Björn D. Lindahl and Cheryl R. Kuske 14 Metatranscriptomics of Soil Eukaryotic Communities 305 Laurence Fraissinet-Tachet, Roland Marmeisse, Lucie Zinger, and Patricia Luis 15 Fungi in Deep-Sea Environments and Metagenomics 325 Stéphane Mahé, Vanessa Rédou, Thomas Le Calvez, Philippe Vandenkoornhuyse, and Gaëtan Burgaud 16 The Biodiversity, Ecology, and Biogeography of Ascomycetous Yeasts 355 Marc-André Lachance Index 371

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Book SynopsisWith the first draft of the human genome project in the public domain and full analyses of model genomes now available, the subject matter of ''Principles of Genome Analysis and Genomics'' is even ''hotter'' now than when the first two editions were published in 1995 and 1998. In the new edition of this very practical guide to the different techniques and theory behind genomes and genome analysis, Sandy Primrose and new author Richard Twyman provide a fresh look at this topic. In the light of recent exciting advancements in the field, the authors have completely revised and rewritten many parts of the new edition with the addition of five new chapters. Aimed at upper level students, it is essential that in this extremely fast moving topic area the text is up to date and relevant. Completely revised new edition of an established textbook. Features new chapters and examples from exciting new research in genomics, including the human genome project. ExcelTrade Review"...an excellent distillation of current knowledge...The book is clearly written, well presented, and feels good. Recommended." Neil Stoker, Royal Veterninary College, London, Microbiology Today, Vol 30, November 2003 "There is no doubt that this book is a very useful source of information, for students and teachers alike. In spite of its dense text, it makes good reading and will help to reduce the general bewilderment induced by the rapid pace of technological and conceptual innovation in biology." Leon Otten, Plant Molecular Biology Institute, Plant Science, 2003. Principles of Genome Analysis and Genomics, 3rd edition, is "very good indeed and deserves to be a widely popular resource for newcomers to genome analysis." J.Armour, University of Nottingham, Heredity, June 2004 Table of ContentsPreface. Abbreviations. 1. Setting The Scene: The New Science Of Genomics. 2. The Organization And Structure Of Genomes. 3. Subdividing The Genome. 4. Assembling A Physical Map Of The Genome. 5. Sequencing Methods And Strategies. 6. Genome Annotation And Bioinformatics. 7. Comparative Genomics. 8. Protein Structural Genomics. 9. Global Expression Profiling. 10. Comprehensive Mutant Libraries. 11. Mapping Protein Interactions. 12. Applications Of Genome Analysis And Genomics. References. Index

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Book SynopsisThis guide covers aspects of designing microarray experiments and analysing the data generated, including information on some of the tools that are available from non-commercial sources. Concepts and principles underpinning gene expression analysis are emphasised and wherever possible, the mathematics has been simplified. The guide is intended for use by graduates and researchers in bioinformatics and the life sciences and is also suitable for statisticians who are interested in the approaches currently used to study gene expression. Microarrays are an automated way of carrying out thousands of experiments at once, and allows scientists to obtain huge amounts of information very quickly Short, concise text on this difficult topic area Clear illustrations throughout Written by well-known teachers in the subject Provides insight into how to analyse the data produced from microarrays Trade Review"Quite a few recently published books discuss analysis of microarray gene expression data for beginners. Microarray Gene Expression Data Analysis ... is arguably the best of its kind in this regard." Terry Speed, The Walter & Eliza Hall Institute of Medical Research, Nature Cell Biology, December 2003 "Overall this is an excellent book, it is well referenced and, to my mind, covers the vast majority of issues an experimenter needs to consider when venturing into the world of microarray data analysis. The book fills a clear gap in the field, providing a rigorous overview of the often confusing .... data analysis issues that most books on microarrays avoid or treat in a cursory way. I would say it is essential reading for any laboratory or researcher active in this rapidly evolving field and is recommended for the mathematician or statisitican who is interested in the field or who has been persuaded by their biologist colleague to help them with their analysis." Steven Russell, University of Cambridge, Genetical Research, February 2003 "Anyone wishing to gain a basic understanding of microarray gene expression studies will come away enriched ... A good and accessible entry point for any biologist who is interested in getting an overview about how to perform microarray gene expression studies." D.C.Jamison, George Mason University, Heredity, June 2004Table of ContentsPreface. Acknowledgements. Part I: Introduction:. 1. What Are Microarrays?. 2. Use Of Icroarrays To Monitor Gene Expression. 3. Other Uses For Microarrays. 4. Challenges Associated With The Generation Of Large Amounts Of Complex Data. 5. Future Directions. Part II: Aspects Of Experimental Design:. 6. Features Of Microarray Data. 7. Designing The Best Experiment. 8. Preparation of Target. 9. Design of Spotted Arrays. 10. Hybridisation. 11. Long Term Considerations. 12. Verification of Results. Part III: Data Analysis:. 13. Preliminary Processing of Data. 14. Methods for Data Analysis. 15. Graph Models. 16. Software In The Public Domain. 17. Visualisation of Data. Part IV: Glossary:. Index. Colour plates fall between pp. 84 and 85.

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Book SynopsisThe structure of DNA varies along its sequence, which can lead to sequence-dependent variations in the fidelity of DNA copying and repair. And because the probability of distinct classes of mutations varies along a DNA sequence, variation that affects fitness will have evolutionary implications, as selection acts on heritable variation. This Annals volume brings together a broad interdisciplinary group of researchers to explore the impact of increasing understanding of DNA structure, repair, replication, and organization on interrelated subjects ranging from evolution, to dependence of the effect of mutagens on environmental and sequence context, to noncanonical forms of information representation in genomes. NOTE: Annals volumes are avaialble for sale as individual books or as a journal. For information on institutional journal subscriptions, please visit http://ordering.onlinelibrary.wiley.com/subs.asp?ref=1749-6632&doi=10.111/(ISSN)1749-6632 ACADEMY MEMBERS: Please contact the New York Academy of Sciences directly to place your order (www.nyas.org). Members of the New York Academy of Science receive full-text access to Annals online and discounts on print volumes. Please visit http://www.nyas.org/MemberCenter/Joun.aspx for more information about becoming a member.Table of ContentsOverview of the creative genome: effects of genome structure and sequence on the generation of variation and evolution 1. Lynn Helena Caporale Genome hyperevolution and the success of a parasite 11. J. David Barry, James P. J. Hall, and Lindsey Plenderleith The tricky path to recombining X and Y chromosomes in meiosis 18. Liisa Kauppi, Maria Jasin, and Scott Keeney Sites of genetic instability in mitosis and cancer 24. Anne M. Casper, Danielle M. Rosen, and Kaveri D. Rajula The genome: an isochore ensemble and it’s evolution 31. Giorgio Bernardi Multiple levels of meaning in DNA sequences, and one more 35. Edward N. Trifonov, Zeev Volkovich, and Zakharia M. Frenkel Evolution of simple sequence repeat-mediated phase variation in bacterial genomes 39. Christopher D. Bayliss and Michael E. Palmer Indirect selection of implicit mutation protocols 45. David G. King G4 motifs in human genes 53. Nancy Maizels Adaptive radiation of venomous marine snail lineages and the accelerated evolution of venom peptide genes 61. Baldomero M. Olivera, Maren Watkins, Pradip Bandyopadhyay, Julita S. Imperial, Edgar P. Heimer de la Cotera, Manual B. Aguilar, Estuardo Lopez Vera, Gisela P. Concepcion, and Arturo Lluisma Integrons and gene cassettes: hotspots of diversity in bacterial genomes 71. Ruth M. Hall Creative deaminases, self-inflicted damage, and genome evolution 79. Silvestro G. Conticello Three-dimensional architecture of the IgH locus facilitates class switch recombination 86. Amy L. Kenter, Scott Feldman, Robert Wuerffel, Ikbel Achour, Lili Wang, and Satyendra Kumar Preaching about the converted: how meiotic gene conversion influences genomic diversity 95. Francesca Cole, Scott Keeney, and Maria Jasin Gross chromosomal rearrangement mediated by DNA replication in stressed cells: evidence from Escherichia coli 103. J.M. Moore, Hallie Wimberly, P.C. Thorton, Susan M. Rosenberg, and P.J. Hastings Implications of genetic heterogeneity in cancer 110. Michael W. Schmitt, Marc J. Prindle, and Lawrence A. Loeb Corrigendum for Ann. N.Y. Acad. Sci. 2009. 1182: 47-57 117.

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Book SynopsisThe study of biological data is constantly undergoing profound changes. Firstly, the volume of data available has increased considerably due to new high throughput techniques used for experiments. Secondly, the remarkable progress in both computational and statistical analysis methods and infrastructures has made it possible to process these voluminous data. The resulting challenge concerns our ability to integrate these data, i.e. to use their complementary nature effectively in the hope of advancing our knowledge. Therefore, a major challenge in studying biology today is integrating data for the most exhaustive analysis possible. Biological Data Integration deals in a pedagogical way with research work in biological data science, examining both computational approaches to data integration and statistical approaches to the integration of omics data.Table of ContentsPreface xiChristine FROIDEVAUX, Marie-Laure MARTIN-MAGNIETTE and Guillem RIGAILL Part 1 Knowledge Integration 1 Chapter 1 Clinical Data Warehouses 3Maxime WACK and Bastien RANCE 1.1 Introduction to clinical information systems and biomedical warehousing: data warehouses for what purposes? 3 1.1.1 Warehouse history 4 1.1.2 Using data warehouses today 4 1.2 Challenge: widely scattered data 5 1.3 Data warehouses and clinical data 6 1.3.1 Warehouse structures 6 1.3.2 Warehouse construction and supply 11 1.3.3 Uses 11 1.4 Warehouses and omics data: challenges 15 1.4.1 Challenges of data volumetry and structuring omic data 16 1.4.2 Attempted solutions 17 1.5 Challenges and prospects 18 1.5.1 Toward general-purpose warehouses 18 1.5.2 Ethical dimension of the implementation and the use of warehouses 19 1.5.3 Origin and reproducibility 19 1.5.4 Data quality 20 1.5.5 Data warehousing federation and data sharing 21 1.6 References 21 Chapter 2 Semantic Web Methods for Data Integration in Life Sciences 25Olivier DAMERON 2.1 Data-related requirements in life sciences 26 2.1.1 Databases for the life sciences 26 2.1.2 Requirements 27 2.1.3 Common approaches: InterMine and BioMart 30 2.2 Semantic Web 31 2.2.1 Techniques 32 2.2.2 Implementation 42 2.3 Perspectives 43 2.3.1 Facilitating appropriation to users 43 2.3.2 Facilitating the appropriation by software programs: FAIR data 44 2.3.3 Federated queries 45 2.4 Conclusion 46 2.5 References 47 Chapter 3 Workflows for Bioinformatics Data Integration 53Sarah COHEN-BOULAKIA and Frédéric LEMOINE 3.1 Introduction 53 3.2 Bioinformatics data processing chains: difficulties 54 3.2.1 Designing a data processing chain 55 3.2.2 Analysis execution and reproducibility 56 3.2.3 Maintenance, sharing and reuse 58 3.3 Solutions provided by scientific workflow systems 59 3.3.1 Fundamentals of workflow systems 59 3.3.2 Workflow systems 64 3.4 Use case: RNA-seq data analysis 69 3.4.1 Study description 69 3.4.2 From data processing chain to workflows 72 3.4.3 Data processing chains implemented as workflows: conclusion 75 3.5 Challenges, open problems and research opportunities 77 3.5.1 Formalizing workflow development 77 3.5.2 Workflow testing 78 3.5.3 Discovering and sharing workflows 79 3.6 Conclusion 80 3.7 References 81 Part 2 Integration and Statistics 87 Chapter 4 Variable Selection in the General Linear Model: Application to Multiomic Approaches for the Study of Seed Quality 89Céline LÉVY-LEDUC, Marie PERROT-DOCKÈS, Gwendal CUEFF and Loïc RAJJOU 4.1 Introduction 90 4.2 Methodology 93 4.2.1 Estimation of the covariance matrix Σq 93 4.2.2 Estimation of B 96 4.3 Numerical experiments 99 4.3.1 Statistical performance 99 4.3.2 Numerical performance 100 4.4 Application to the study of seed quality 103 4.4.1 Metabolomics data 104 4.4.2 Proteomics data 105 4.5 Conclusion 108 4.6 Appendices 108 4.6.1 Example of using the package MultiVarSel for metabolomic data analysis 108 4.6.2 Example of using the package MultiVarSel for proteomic data analysis 110 4.7 Acknowledgments 113 4.8 References 113 Chapter 5 Structured Compression of Genetic Information and Genome-Wide Association Study by Additive Models 117Florent GUINOT, Marie SZAFRANSKI and Christophe AMBROISE 5.1 Genome-wide association studies 118 5.1.1 Introduction to genetic mapping and linkage analysis 118 5.1.2 Principles of genome-wide association studies 119 5.1.3 Single nucleotide polymorphism 120 5.1.4 Disease penetrance and odds ratio 122 5.1.5 Single marker analysis 124 5.1.6 Multi-marker analysis 126 5.2 Structured compression and association study 132 5.2.1 Context 132 5.2.2 New structured compression approach 133 5.3 Application to ankylosing spondylitis (AS) 142 5.3.1 Data 142 5.3.2 Predictive power evaluation 143 5.3.3 Manhattan diagram 144 5.3.4 Estimation for the most significant SNP aggregates 144 5.4 Conclusion 146 5.5 References 146 Chapter 6 Kernels for Omics 151Jérôme MARIETTE and Nathalie VIALANEIX 6.1 Introduction 152 6.2 Relational data 153 6.2.1 Data described by the kernel 153 6.2.2 Data described by a general (dis)similarity measure 155 6.3 Exploratory analysis for relational data 158 6.3.1 Kernel clustering 158 6.3.2 Kernel principal component analysis 161 6.3.3 Kernel self-organizing maps 163 6.3.4 Limitations of relational methods 166 6.4 Combining relational data 168 6.4.1 Data integration in systems biology 168 6.4.2 Kernel approaches in data integration 169 6.4.3 A consensual kernel 172 6.4.4 A parsimonious kernel that preserves the topology of the initial data 173 6.4.5 A complete kernel preserving the topology of the initial data 175 6.5 Application 176 6.5.1 Loading Tara Ocean data 176 6.5.2 Data integration by kernel approaches 177 6.5.3 Exploratory analysis: kernel PCA 179 6.6 Session information for the results of the example 186 6.7 References 188 Chapter 7 Multivariate Models for Data Integration and Biomarker Selection in ‘Omics Data 195Sébastien DÉJEAN and Kim-Anh LÊ CAO 7.1 Introduction 195 7.2 Background 197 7.2.1 Mathematical notations 197 7.2.2 Terminology 198 7.2.3 Multivariate projection-based approaches 198 7.2.4 A criterion to maximize specific to each methodology 199 7.2.5 A linear combination of variables to reduce the dimension of the data 199 7.2.6 Identifying a subset of relevant molecular features 200 7.2.7 Summary 200 7.3 From the biological question to the statistical analysis 201 7.3.1 Exploration of one dataset: PCA 201 7.3.2 Classify samples: projection to latent structure discriminant analysis 206 7.3.3 Integration of two datasets: projection to latent structure and related methods 210 7.3.4 Integration of several datasets: multi-block approaches 215 7.4 Graphical outputs 220 7.4.1 Individual plots 220 7.4.2 Variable plots 221 7.5 Overall summary 222 7.6 Liver toxicity study 223 7.6.1 The datasets 223 7.6.2 Biological questions and statistical methods 223 7.6.3 Single dataset analysis 224 7.6.4 Integrative analysis 231 7.7 Conclusion 238 7.8 Acknowledgments 238 7.9 Appendix: reproducible R code 239 7.9.1 Toy examples 239 7.9.2 Liver toxicity 243 7.10 References 247 List of Authors 251 Index 255

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Book SynopsisComplexes of physically interacting proteins constitute fundamental functional units that drive almost all biological processes within cells. A faithful reconstruction of the entire set of protein complexes (the "complexosome") is therefore important not only to understand the composition of complexes but also the higher level functional organization within cells. Advances over the last several years, particularly through the use of high-throughput proteomics techniques, have made it possible to map substantial fractions of protein interactions (the "interactomes") from model organisms including Arabidopsis thaliana (a flowering plant), Caenorhabditis elegans (a nematode), Drosophila melanogaster (fruit fly), and Saccharomyces cerevisiae (budding yeast). These interaction datasets have enabled systematic inquiry into the identification and study of protein complexes from organisms. Computational methods have played a significant role in this context, by contributing accurate, efficient, and exhaustive ways to analyze the enormous amounts of data. These methods have helped to compensate for some of the limitations in experimental datasets including the presence of biological and technical noise and the relative paucity of credible interactions.In this book, we systematically walk through computational methods devised to date (approximately between 2000 and 2016) for identifying protein complexes from the network of protein interactions (the protein-protein interaction (PPI) network). We present a detailed taxonomy of these methods, and comprehensively evaluate them for protein complex identification across a variety of scenarios including the absence of many true interactions and the presence of false-positive interactions (noise) in PPI networks. Based on this evaluation, we highlight challenges faced by the methods, for instance in identifying sparse, sub-, or small complexes and in discerning overlapping complexes, and reveal how a combination of strategies is necessary to accurately reconstruct the entire complexosome.Table of Contents Preface 1. Introduction to Protein Complex Prediction 2. Constructing Reliable Protein-Protein Interaction (PPI) Networks 3. Computational Methods for Protein Complex Prediction from PPI Networks 4. Evaluating Protein Complex Prediction Methods 5. Open Challenges in Protein Complex Prediction 6. Identifying Dynamic Protein Complexes 7. Identifying Evolutionarily Conserved Protein Complexes 8. Protein Complex Prediction in the Era of Systems Biology 9. Conclusion References Authors' Biographies

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Book SynopsisComplexes of physically interacting proteins constitute fundamental functional units that drive almost all biological processes within cells. A faithful reconstruction of the entire set of protein complexes (the "complexosome") is therefore important not only to understand the composition of complexes but also the higher level functional organization within cells. Advances over the last several years, particularly through the use of high-throughput proteomics techniques, have made it possible to map substantial fractions of protein interactions (the "interactomes") from model organisms including Arabidopsis thaliana (a flowering plant), Caenorhabditis elegans (a nematode), Drosophila melanogaster (fruit fly), and Saccharomyces cerevisiae (budding yeast). These interaction datasets have enabled systematic inquiry into the identification and study of protein complexes from organisms. Computational methods have played a significant role in this context, by contributing accurate, efficient, and exhaustive ways to analyze the enormous amounts of data. These methods have helped to compensate for some of the limitations in experimental datasets including the presence of biological and technical noise and the relative paucity of credible interactions.In this book, we systematically walk through computational methods devised to date (approximately between 2000 and 2016) for identifying protein complexes from the network of protein interactions (the protein-protein interaction (PPI) network). We present a detailed taxonomy of these methods, and comprehensively evaluate them for protein complex identification across a variety of scenarios including the absence of many true interactions and the presence of false-positive interactions (noise) in PPI networks. Based on this evaluation, we highlight challenges faced by the methods, for instance in identifying sparse, sub-, or small complexes and in discerning overlapping complexes, and reveal how a combination of strategies is necessary to accurately reconstruct the entire complexosome.Table of Contents Preface 1. Introduction to Protein Complex Prediction 2. Constructing Reliable Protein-Protein Interaction (PPI) Networks 3. Computational Methods for Protein Complex Prediction from PPI Networks 4. Evaluating Protein Complex Prediction Methods 5. Open Challenges in Protein Complex Prediction 6. Identifying Dynamic Protein Complexes 7. Identifying Evolutionarily Conserved Protein Complexes 8. Protein Complex Prediction in the Era of Systems Biology 9. Conclusion References Authors' Biographies

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Book SynopsisThis invaluable textbook presents a self-contained introduction to the field of bioinformatics. Providing a comprehensive breadth of coverage while remaining accessibly concise, the text promotes a deep understanding of the field, supported by basic mathematical concepts, an emphasis on biological knowledge, and a holistic approach that highlights the connections unifying bioinformatics with other areas of science.The thoroughly revised and enhanced fourth edition features new chapters focusing on regulation and control networks, the origins of life, evolution, statistics and causation, viruses, the microbiome, single cell analysis, drug discovery and forensic applications. This edition additionally includes new and updated material on the ontology of bioinformatics, data mining, ecosystems, and phenomics. Also covered are new developments in sequencing technologies, gene editing methods, and modelling of the brain, as well as state-of-the-art medical applications. Of special topicality is a new chapter on bioinformatics aspects of the coronavirus pandemic.Topics and features: Explains the fundamentals of set theory, combinatorics, probability, likelihood, causality, clustering, pattern recognition, randomness, complexity, systems, and networks Discusses topics on ontogeny, phylogeny, genome structure, and regulation, as well as aspects of molecular biology Critically examines the most significant practical applications, offering detailed descriptions of both the experimental process and the analysis of the data Provides a varied selection of problems throughout the book, to stimulate further thinking Encourages further reading through the inclusion of an extensive bibliography This classic textbook builds upon the successful formula of previous editions with coverage of the latest advances in this exciting and fast-moving field. With its interdisciplinary scope, this unique guide will prove to be an essential study companion to a broad audience of undergraduate and beginning graduate students, spanning computer scientists focusing on bioinformatics, students of the physical sciences seeking a helpful primer on biology, and biologists desiring to better understand the theory underlying important applications of information science in biology.Dr. Jeremy Ramsden is Hon. Prof. of Nanotechnology in the Department of Biomedical Research at the University of Buckingham, UK.Table of Contents

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Book SynopsisThis 5th edition of this essential textbook continues to meet the growing demand of practitioners, researchers, educators, and students for a comprehensive introduction to key topics in biomedical informatics and the underlying scientific issues that sit at the intersection of biomedical science, patient care, public health and information technology (IT). Emphasizing the conceptual basis of the field rather than technical details, it provides the tools for study required for readers to comprehend, assess, and utilize biomedical informatics and health IT. It focuses on practical examples, a guide to additional literature, chapter summaries and a comprehensive glossary with concise definitions of recurring terms for self-study or classroom use.Biomedical Informatics: Computer Applications in Health Care and Biomedicine reflects the remarkable changes in both computing and health care that continue to occur and the exploding interest in the role that IT must play in care coordination and the melding of genomics with innovations in clinical practice and treatment. New and heavily revised chapters have been introduced on human-computer interaction, mHealth, personal health informatics and precision medicine, while the structure of the other chapters has undergone extensive revisions to reflect the developments in the area. The organization and philosophy remain unchanged, focusing on the science of information and knowledge management, and the role of computers and communications in modern biomedical research, health and health care.Table of ContentsBiomedical Informatics: The Science and the Pragmatics.- Biomedical Data: Their Acquisition, Storage, and Use.- Biomedical Decision Making: Probabilistic Clinical Reasoning.- Cognitive Science and Biomedical Informatics.- Computer Architectures for Health Care and Biomedicine.- Software Engineering for Health Care and Biomedicine.- Standards in Biomedical Informatics.- Natural Language Processing in Health Care and Biomedicine.- Biomedical Imaging Informatics.- Ethics and Biomedical and Health Informatics: Users, Standards, and Outcomes.- Evaluation of Biomedical and Health Information Resources.- Electronic Health Record Systems.- The Health Information Infrastructure.- Management of Information in Health Care Organizations.- Patient-Centered Care Systems.- Public Health Informatics.- Consumer Health Informatics and Personal Health Records.- Telehealth.- Patient Monitoring Systems.- Imaging Systems in Radiology.- Information Retrieval and Digital Libraries.- Clinical Decision-Support Systems.- Computers in Health Care Education.- Bioinformatics.- Translational Bioinformatics.- Clinical Research Informatics.- Health Information Technology Policy.- The Future of Informatics in Biomedicine.

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Book SynopsisThis edited book explores the use of technology to enable us to visualise the life sciences in a more meaningful and engaging way. It will enable those interested in visualisation techniques to gain a better understanding of the applications that can be used in visualisation, imaging and analysis, education, engagement and training. The reader will also be able to learn about the use of visualisation techniques and technologies for the historical and forensic settings.The reader will be able to explore the utilisation of technologies from a number of fields to enable an engaging and meaningful visual representation of the biomedical sciences.In this volume, there are chapters which examine forensic and historical visualisation techniques and digital reconstruction, ultrasound, virtual learning resources and patient utilised software and hardware. The use of HoloLens as a disruptive technology is discussed as well as historical items as a feature in a modern medical curriculum. It concludes with a fascinating chapter on pulse extraction from facial videos. All in all, this volume has something for everyone whether that is faculty, students, clinicians and forensic practitioners, patients, or simply having an interest in one or more of these areas.Table of ContentsPlease see attachment

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Book SynopsisThis book gathers papers presented at the Workshop on Computational Diffusion MRI, CDMRI 2020, held under the auspices of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), which took place virtually on October 8th, 2020, having originally been planned to take place in Lima, Peru.This book presents the latest developments in the highly active and rapidly growing field of diffusion MRI. While offering new perspectives on the most recent research challenges in the field, the selected articles also provide a valuable starting point for anyone interested in learning computational techniques for diffusion MRI. The book includes rigorous mathematical derivations, a large number of rich, full-colour visualizations, and clinically relevant results. As such, it is of interest to researchers and practitioners in the fields of computer science, MRI physics, and applied mathematics. The reader will find numerous contributions covering a broad range of topics, from the mathematical foundations of the diffusion process and signal generation to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as diffusion-relaxometry and frontline applications in research and clinical practice.Table of Contents

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Book SynopsisMicrobiome research has focused on microorganisms that live within the human body and their effects on health. During the last few years, the quantification of microbiome composition in different environments has been facilitated by the advent of high throughput sequencing technologies. The statistical challenges include computational difficulties due to the high volume of data; normalization and quantification of metabolic abundances, relative taxa and bacterial genes; high-dimensionality; multivariate analysis; the inherently compositional nature of the data; and the proper utilization of complementary phylogenetic information. This has resulted in an explosion of statistical approaches aimed at tackling the unique opportunities and challenges presented by microbiome data. This book provides a comprehensive overview of the state of the art in statistical and informatics technologies for microbiome research. In addition to reviewing demonstrably successful cutting-edge methods, particular emphasis is placed on examples in R that rely on available statistical packages for microbiome data. With its wide-ranging approach, the book benefits not only trained statisticians in academia and industry involved in microbiome research, but also other scientists working in microbiomics and in related fields.Table of Contents1. Tree-guided regression and multivariate analysis of microbiome data - Hongu Zhao and Tao Wang.- 2. Computational methods for metagenomic assemblies and strain identification - Hongzhe Li.- 3. Graphical models for microbiome data - Ali Shojaie.- 4. Bayesian models for understanding the modulating factors of microbiome data - Francesco Denti, Matthew D. Koslovsky, Michele Guindani, Marina Vannucci, and Katrine L. Whiteson.- 5. Use of variable importance in microbiome studies - Hemant Ishwaran.- 6. Log-linear models for microbiome data - Glen Satten.- 7. Quantification of amplicon sequences in microbiome samples using statistical methods - Karin Dorman.- 8. TBD - Jeanine Houwing Duistermaat.- 9. Analyzing microbiome data by employing the power of abundance ratios - Zhigang Li.- 10. Beta diversity analysis - Michael Wu.- 11. MicroPro: using metagenomic unmapped reads to provide insights into human microbiota and disease associations - Fengzhu Sun.- 12. Statistical methods for feature selection in microbiome studies - Peng Liu.- 13. A Bayesian restoration of the duality between principal components of a distance matrix and operational taxonomic units in microbiome analyses - Somnath Datta and Subharup Guha.

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Book SynopsisThis open access textbook aims at providing detailed explanations on how to design and construct image analysis workflows to successfully conduct bioimage analysis. Addressing the main challenges in image data analysis, where acquisition by powerful imaging devices results in very large amounts of collected image data, the book discusses techniques relying on batch and GPU programming, as well as on powerful deep learning-based algorithms. In addition, downstream data processing techniques are introduced, such as Python libraries for data organization, plotting, and visualizations. Finally, by studying the way individual unique ideas are implemented in the workflows, readers are carefully guided through how the parameters driving biological systems are revealed by analyzing image data. These studies include segmentation of plant tissue epidermis, analysis of the spatial pattern of the eye development in fruit flies, and the analysis of collective cell migration dynamics. The presented content extends the Bioimage Data Analysis Workflows textbook (Miura, Sladoje, 2020), published in this same series, with new contributions and advanced material, while preserving the well-appreciated pedagogical approach adopted and promoted during the training schools for bioimage analysis organized within NEUBIAS – the Network of European Bioimage Analysts. This textbook is intended for advanced students in various fields of the life sciences and biomedicine, as well as staff scientists and faculty members who conduct regular quantitative analyses of microscopy images. Table of ContentsIntroduction.- Batch Processing Methods in ImageJ.- Python: Data Handling, Analysis and Plotting.- Building a Bioimage Analysis Workflow Using Deep Learning.- GPU-Accelerating ImageJ Macro Image Processing Workflows Using CLIJ.- How to Do the Deconstruction of Bioimage Analysis Workflows: A Case Study with SurfCut.- i.2.i. with the (Fruit) Fly: Quantifying Position Effect Variegation in Drosophila Melanogaster.- A MATLAB Pipeline for Spatiotemporal Quantification of Monolayer Cell Migration.

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Book SynopsisThis essential book details intermediate-level statistical methods and frameworks for the clinician and medical researcher with an elementary grasp of health statistics and focuses on selecting the appropriate statistical method for many scenarios. Detailed evaluation of various methodologies familiarizes readers with the available techniques and equips them with the tools to select the best from a range of options. The inclusion of a hypothetical case study between a clinician and statistician charting the conception of the research idea through to results dissemination enables the reader to understand how to apply the concepts covered into their day-to-day clinical practice.Applied Statistical Considerations for Clinical Researchers focuses on how clinicians can approach statistical issues when confronted with a medical research problem by considering the data structure, how this relates to their study's aims and any potential knock-on effects relating to the evidence required to make correct clinical decisions. It covers the application of intermediate-level techniques in health statistics making it an ideal resource for the clinician seeking an up-to-date resource on the topic.Table of ContentsIntroduction.- Preliminaries.- Design.- Planning.- Data Acquisition.- Data Manipulation Analysis.- Inferencesty.- Dissemination.- A Case Study.- Conclusions.

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Book SynopsisThis book presents a set of soft computing approaches and their application in data analytics, classification model, and control. The basics of fuzzy logic implementation for advanced hybrid fuzzy driven optimization methods has been covered in the book. The various soft computing techniques, including Fuzzy Logic, Rough Sets, Neutrosophic Sets, Type-2 Fuzzy logic, Neural Networks, Generative Adversarial Networks, and Evolutionary Computation have been discussed and they are used on variety of applications including data analytics, classification model, and control. The book is divided into two thematic parts. The first thematic section covers the various soft computing approaches for text classification and data analysis, while the second section focuses on the fuzzy driven optimization methods for the control systems. The chapters has been written and edited by active researchers, which cover hypotheses and practical considerations; provide insights into the design of hybrid algorithms for applications in data analytics, classification model, and engineering control.Table of ContentsChapter 1: An Optimization of Fuzzy Rough Set Nearest Neighbor Classification Model using Krill Herd Algorithm for Sentiment Text Analytics.- Chapter 2: Fuzzy Wavelet Neural Network with Social Spider Optimization Algorithm for Pattern Recognition in Medical Domain.- Chapter 3: Fuzzy with Gravitational Search Algorithm Tuned Radial Basis Function Network for Medical Disease Diagnosis and Classification Model.- Chapter 4: Optimal Neutrosophic Rules based Feature Extraction for Data Classification using Deep Learning Model.- Chapter 5: Self-Evolving Interval Type-2 Fuzzy Neural Network Design for The Synchronization of Chaotic Systems.- Chapter 6: Categorizing Relations via Semi-Supervised Learning using a Hybrid Tolerance Rough Sets and Genetic Algorithm Approach.- Chapter 7: Data-driven Fuzzy C-Means Equivalent Turbine-governor for Power System Frequency Response.- Chapter 8: Multicriteria group decision making using a novel similarity measure for triangular fuzzy numbers based on their newly defined expected values and variances.- Chapter 9: Bangla Printed Character Generation from Handwritten Character Using GAN.

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Book SynopsisThis open access book bridges common tools in medical imaging and neuroscience with the numerical solution of brain modelling PDEs. The connection between these areas is established through the use of two existing tools, FreeSurfer and FEniCS, and one novel tool, the SVM-Tk, developed for this book. The reader will learn the basics of magnetic resonance imaging and quickly proceed to generating their first FEniCS brain meshes from T1-weighted images. The book's presentation concludes with the reader solving a simplified PDE model of gadobutrol diffusion in the brain that incorporates diffusion tensor images, of various resolution, and complex, multi-domain, variable-resolution FEniCS meshes with detailed markings of anatomical brain regions. After completing this book, the reader will have a solid foundation for performing patient-specific finite element simulations of biomechanical models of the human brain.Trade Review“The book represents an excellent introduction and hands-on guide to this important and exciting field, for applied mathematicians and image processing practitioners … . It is, perhaps, most beneficial, to electrical and computer science majors who wish to rapidly immerse themselves in the field, in a manner that is mathematically correct and sound, yet also practical.” (Emil Saucan, zbMATH 1501.92001, 2023)Table of ContentsIntroduction.- Working with magnetic resonance images of the brain.- From T1 images to numerical simulation.- Introducing heterogeneities.- Introducing directionality with diffusion tensors.- Simulating anisotropic diffusion in heterogeneous brain regions.- Concluding remarks and outlook.- References.- Index.

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Book SynopsisThis book gathers selected papers from the First International Symposium on Bioinformatics and Biomedicine. Issues related to medicine and health care constitute one of the grand challenges faces by the mankind, and this naturally implies a growing interest in these problems among both researchers and scholars, politicians and policymakers, as well as economist. The present values which gather selected papers from the First International Symposium on Bioinformatics and Biomedicine (BioInfoMed’2020) is a recent response to this interests. In the subsequent sections and chapters, it covers a multitude of various topics related to bioinformatics, biomedicine, bioenginering, as well as a broadly perceives healthcare. Issues related to decision making in bioinformatics, biomedicine and health care, mathematical modelling in biomedicine and health care, artificial intelligence in biomedicine and health care, uncertainty and imprecision, notably intuitionistic fuzzy sets with applications in bioinformatics and biomedicine, biomedical approaches and applications, biomedical imaging and image processing, and excitable structures and motor activity are covered.

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Book SynopsisThis book constitutes the refereed proceedings of the 19th Annual RECOMB Satellite Workshop on Comparative Genomics, RECOMB-CG which took place in La Jolla, USA, during May 20-21, 2022. The 18 full papers included in this book were carefully reviewed and selected from 28 submissions. The papers were organized in topical sections on evolution; phylogenetics; homology and reconciliation; genome rearrangements; metagenomics; and genomic sequencing. Table of ContentsEvolution.- On the Comparison of Bacteriophage Populations.- Syntenic Dimensions of Genomic Evolution.- Phylogenetics.- Fast and Accurate Branch Support Calculation for Distance-based Phylogenetic Placements.- The Sackin Index of Simplex Networks.- Phylogenetic Placement Problem: A Hyperbolic Embedding Approach.- Phylogenetic Network Dissimilarity Measures That Take Branch Lengths Into Account.- Homology and Reconciliation.- The Complexity of Finding Common Partitions of Genomes with Predefined Block Sizes.- Reconciliation with Segmental Duplication, Transfer, Loss and Gain.- Quantifying Hierarchical Conflicts in Homology Statements.- On Partial Gene Transfer and its Impact on Gene Tree Reconstruction.- Genome Rearrangements.- Sorting by k-Cuts on Signed Permutations.- A New Approach for the Reversal Distance with Indels and Moves in Intergenic Regions.- Chromothripsis Rearrangements Are Informed by 3D Genome Organization.- Metagenomics.- Using Computational Synthetic Biology Tools to Modulate Gene Expression within a Microbiome.- Metagenomics Binning of Long Reads Using Read-Overlap Graphs.- A Mixed Integer Linear Programming Algorithm for Plasmid Binning.- Genomic Sequencing.- Benchmarking Penalized Regression Methods in Machine Learning for Single Cell RNA Sequencing Data.- Deciphering the Tissue-speci c Regulatory Role of Intronless Genes Across Cancers.

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Book SynopsisMathematical Modeling for Epidemiology and Ecology provides readers with the mathematical tools needed to understand and use mathematical models and read advanced mathematical biology books. It presents mathematics in biological contexts, focusing on the central mathematical ideas and the biological implications, with detailed explanations. The author assumes no mathematics background beyond elementary differential calculus. An introductory chapter on basic principles of mathematical modeling is followed by chapters on empirical modeling and mechanistic modeling. These chapters contain a thorough treatment of key ideas and techniques that are often neglected in mathematics books, such as the Akaike Information Criterion. The second half of the book focuses on analysis of dynamical systems, emphasizing tools to simplify analysis, such as the Routh-Hurwitz conditions and asymptotic analysis. Courses can be focused on either half of the book or thematically chosen material from both halves, such as a course on mathematical epidemiology.The biological content is self-contained and includes many topics in epidemiology and ecology. Some of this material appears in case studies that focus on a single detailed example, and some is based on recent research by the author on vaccination modeling and scenarios from the COVID-19 pandemic.The problem sets feature linked problems where one biological setting appears in multi-step problems that are sorted into the appropriate section, allowing readers to gradually develop complete investigations of topics such as HIV immunology and harvesting of natural resources. Some problems use programs written by the author for Matlab or Octave; these combine with more traditional mathematical exercises to give students a full set of tools for model analysis. Each chapter contains additional case studies in the form of projects with detailed directions. New appendices contain mathematical details on optimization, numerical solution of differential equations, scaling, linearization, and sophisticated use of elementary algebra to simplify problems.Trade Review“This is a well-written book, highly suitable for applied math undergraduate students.” (Stephanie Abo, Chi-Chung Cheung, Ryth Dasgupta, Pritha Dutta, Shervin Hakimi, Amandeep Kaur, Anita T. Layton, Mehrshad Sadria, Melissa Stadt, Vasu Swaroop and Kaixin Zheng)Table of ContentsPart I Mathematical Modeling.- 1 Modeling in Biology.- 2 Empirical Modeling.- 3 Mechanistic Modeling. Part II Dynamical Systems.- 4 Dynamics of Single Populations.- 5 Discrete Linear Systems.- 6 Nonlinear Dynamical Systems.- Appendix A. Using Matlab and Octave.- Appendix B. Derivatives and Differentiation.- Appendix C. Nonlinear Optimization.- Appendix D. A Runge-Kutta Method for Numerical Solution of Differential Equations.- Appendix E. Scales and Dimensionless Parameters.- Appendix F. Approximating a Nonlinear System at an Equilibrium Point.- Appendix G. Best Practices in the Use of Algebra.- Hints and Answers to Selected Problems.- Index.

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Book SynopsisThis unique book addresses the bioinformatic and statistical modelling and also the analysis of microbiome data using cutting-edge QIIME 2 and R software. It covers core analysis topics in both bioinformatics and statistics, which provides a complete workflow for microbiome data analysis: from raw sequencing reads to community analysis and statistical hypothesis testing. It includes real-world data from the authors’ research and from the public domain, and discusses the implementation of QIIME 2 and R for data analysis step-by-step. The data as well as QIIME 2 and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter so that these new methods can be readily applied in their own research. Bioinformatic and Statistical Analysis of Microbiome Data is an ideal book for advanced graduate students and researchers in the clinical, biomedical, agricultural, and environmental fields, as well as those studying bioinformatics, statistics, and big data analysis.Table of ContentsChapter 1: Introduction to Linux and Unix(This chapter will introduce some important bioinformatics tools and basics of Linux/Unix system and basic operations with Linux/Unix.) 1.1. Bioinformatics tools and Linux/Unix1.2. Features of Linux/Unix1.3. Interact with Linux/Unix Chapter 2: Introduction to R, RStudio(This chapter will introduce the environment of microbiome data analysis: R, RStudio, and some important R functions and data manipulation skills. All these skills will provide a foundation of bioinformatic and biostatistical analyses of microbiome data.) 2.1. Introduction to R and RStudio2.1.1 Installing R, RStudio, and R Packages2.1.2 Set Working Directory in R2.1.3 Data Analysis through R Studio 2.1.4 Data Import and Export 2.1.5 Basic Data Manipulation2.1. 6 Simple Summary Statistics2.1.7 Other useful R functions2.2. Useful R Packages for Data Management Chapter 3: Bioinformatic Analysis of Next-Generation Sequencing(This chapter will cover next-generation sequencing (NGS) and bioinformatic analysis of NGS data, such as sequencing data quality check, trimming, gene annotation, sequencing alignment, and genome indexing.) 3.1. Introduction to Next-Generation Sequencing3.2. Bioinformatic Analysis of Next-Generation Sequencing3.2.1 Sequencing Data Quality Check3.2.2 Sequencing Data Trimming 3.2.3 Gene Annotation3.2.4 Sequencing Alignment3.2.5 Genome Indexing3.2.6 Remove PCR Duplicates3.3. Introduction to Genome Browsers3.3.1 IGV (Integrative Genome Brower)3.3.2 UCSC Chapter 4: Bioinformatic Analysis of Metagenomics(This chapter will cover bioinformatic analysis of NGS and metagenomics data step by step. The steps will focus on bioinformatic analysis of amplicon sequencing, such as generate OTUs, taxonomic annotation and create OUT table. ) 4.1 Definition of Metagenomics4.2 Amplicon Sequencing4.2.1 Preprocessing4.2.2 Generate OTUs4.2.3 Taxonomic Annotation4.2.4 Create OUT Table4.3 Bioinformatcs Tools for Amplicon Sequencing4.3.1 QIIME 24.3.2 mothur 4.3.3 Bioinformatic Analysis of 16S rRNA Sequence Data using QIIME 2 and mothur4.4 Bioinformatic Analysis of Shortgun Metagenomic Data 4.4.1 Processing of Samples, DNA and Library4.4.2 Quality Checking4.4.3 Assembly4.4.4 Binning4.4.5 Annotation4.4.5.1 Genome and Metagenome Functional Annotations4.4.5.2 Gene Prediction and Functional Annotation Chapter 5: Alpha Diversity(This chapter will introduce biostatistical analysis of alpha diversity of microbiome data. The contents will cover alpha diversity measures and calculations, exploration, statistical hypothesis testing, and power analysis.) 5.1 Introduction to Community Diversities5.1.1 Alpha Diversity5.1.2 Beta Diversity5.2 Alpha Diversity Measures and Calculations5.2.1 Chao 1 Richness Index5.2.2 Shannon-Wiener Diversity Index5.2.3 Simpson Diversity Index5.2.4 Pielou's Evenness Index5.3 Exploration of Alpha Diversity5.3.1 Richness5.3.2 Abundance Bar5.3.3 Heatmap5.3.4 Network 5.3.5 Phylogenetic Tree5.4 Statistical Hypothesis Testing of Alpha Diversity 5.4.1 Two-sample Welch's t-test 5.4.2 Wilcoxon Rank Sum Test 5.4.3 Chi-square Test 5.4.4 One-way ANOVA 5.5.5 Kruskal-Wallis Test 5.5 Multiple Comparisons and Multiple Testing 5.5.1 Pairwise Comparisons 5.5.2 E-value 5.5.3 FWER 5.5.4 FDR5.6. Power Analysis for Testing Differences in Diversity5.6.1 Using power.t.test()5.6.2 Using pwr.avova.test()5.6.3 Using power.prop.test() 5.6.4 Using pwr.chisq.test() 5.6.5 Using power.fisher.test() 5.6.6 Using power.exact.test() Chapter 6: Beta Diversity(This chapter will introduce biostatistical analysis of beta diversity of microbiome data. The contents will cover beta diversity measures and calculations, exploration, ordination, statistical hypothesis testing.) 6.1 Beta Diversity Measures and Calculations6.1.1 Jaccard Index6.1.2 Sørensen Index6.1.3 Bray–Curtis Index6.2 Exploration of Beta Diversity6.2.1 Clustering6.2.1.1 Single Linkage6.2.1.2 Complete Linkage6.2.1.3 Average Linkage 6.2.1.4 Ward’s Minimum Variance6.2.2 Ordination6.2.2.1 Principal Component Analysis (PCA)6.2.2.2 Principal Coordinate Analysis (PCoA) 6.2.2.3 Non-metric multidimensional scaling (NMDS)6.4 Statistical Hypothesis Testing of Beta Diversity 6.4.1 Permutational Multivariate Analysis of Variance (PERMANOVA) 6.4.1.1 Implement PERMANOVA using vegan Package 6.4.1.2 Implement Pairwise Permutational MANOVA using RVAideMemoire Package6.4.2 Analysis of Similarity (ANOSIM) 6.4.2.1 Implement ANOSIM using vegan Package 6.4.3 Compare Microbiome Communities 6.4.3.1 UniFrac, Weighted UniFrac and Generalized UniFrac Distance Metrics 6.4.3.2 Implement Comparison using GUniFrac Package Chapter 7: Differential Abundance Analysis(This chapter will cover two models for count-based differential abundance analysis of microbiome data: negative binomial (NB) models in edgeR and in DESeq2.) 7.1. Count-based Differential Abundance Analysis7.1.1 Biological and Technical Variations7.1.2 Poisson 7.1.3 Negative Binomial (NB)7.2 NB Model in edgeR7.2.1 Exploration of Differential Abundant Taxa7.2.1.1 PCoA7.2.1.2 Heatmap7.2.1.3 Volcano Plot7.2.2 Statistical Hypothesis Testing in edgeR7.2.2.1 The Wald Test7.2.2.2 The Generalized Linear model (GLM)7.3. NB Model in DESeq and DESeq27.3.1 Statistical Hypothesis Testing in DESeq2 7.3.2 Implement DESeq2 Chapter 8: Analyzing Zero-Inflated Microbiome Data(This chapter will introduce both classic and newly developed statistical models for analyzing zero-inflated count microbiome data and show how to use different tests to compare these models. ) 8.1 Zero-inflated Models8.1.1 ZIP Model8.1.2 ZINB Model8.2 Zero-Hurdle Models8.2.1 ZHP Model8.2.2 ZHNB Model8.3 Comparison of Zero-inflated and Zero-Hurdle Models8.3.1 Using Likelihood Ratio Test8.3.2 Using AIC8.3.3 Using BIC8.3.4 Using Vuong Test8.4 Zero-inflated Gaussian (ZIG)8.4.1 Statistical Hypothesis Testing 8.4.1.1 Non-parametric Permutation Test on t-statistics 8.4.1.2 Non-parametric Kruskal-Wallis Test8.4.2 Implement using metagenomeSeq package8.5 Marginalized two-part Beta Regression(MTPBR)8.5.1 Introduction to MTPBR8.4.2 Implement using NLMIXED Procedure8.6 Geometric Mean of Pairwise Ratios (GMPR)8.5.1 Introduction to GMPR8.4.2 Implement using GMPR Package Chapter 9: Compositional Analysis of Microbiome Data(This chapter will summarize the issues of compositional data analysis and introduce the newly developed statistical models and methods for compositional data analysis in microbiome research.) 9.1 Introduction to Compositional Data9.1.1 Aitchison Simplex9.1.2 Fundamental Principles9.1.3 A Family of Log-ratio Transformations 9.1.4 Relative Characteristics of Microbiome Abundance Data9.2 ANOVA-Like Differential Abundance Analysis for Compositional Data9.2.1 Exploratory Compositional Data Analysis9.2.1.1 Compositional Biplot9.2.1. 2 Compositional Scree Plot9.2.1. 3 Compositional Cluster Dendrogram 9.2.1. 4 Compositional Barplot 9.2.2 Using ALDEx2 Package9.3 Analysis of Composition of Microbiomes (ANCOM)9.3.1 Introduction to ANCOM 9.3.2 Implement using ANCOM Package9.4 Balances: a Relative Abundances Perspective for Microbiome Analysis9.4.1 Introduction to Balances9.4.2 Implementing Selection of Balances Using selbal Package Chapter 10: Longitudinal Data Analysis of Microbiome(This chapter will introduce several newly developed statistical models and methods for longitudinal data analysis of microbiome.) 10.1 Zero-inflated Beta Regression Model with Random Effects: ZIBR10.1.1 Statistical Hypothesis Testing of ZIBR10.1.2 Implement using ZIBR Package10.2 Differential Distribution Analysis of Microbiome Data10.1.1 A General Framework of Statistical Hypothesis Testing based on a ZINB10.1.2 Implement using MicrobiomeDDA package10.3 Negative Binomial Mixed Models (NBMMs)10.3.1 Introduction to NBMMs10.3.2 Implement using NBZIMMpackage Chapter 11: Meta-analysis of Microbiome Data (optional)(This chapter will summarize current approaches of meta-analysis of microbiome data and discuss the issues of current approaches. The zero-inflated Beta GAMLSS of meta-analysis of microbiome data will be introduced.) 11.1 Introduction to Meta-analysis in Microbiome Studies11.2 Zero-inflated Beta GAMLSS and Meta-analysis of Microbiome Relative Abundance11.3 Implement using metamicrobiomeR package

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