Computational biology / bioinformatics Books

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Book SynopsisFixing Your Damaged and Incorrect Genes is a book about a well-established biological process called DNA REPAIR. The book describes the multiple and varied biochemical strategies by which damaged or incorrect nucleotides are removed from DNA or are corrected. The book includes multiple figures of notable past and present scientists in the field. The book is uniquely focused on an audience of non-biologists and is written in simple language with minimal use of technical terms. It contains an extensive glossary that provides explanations of key words that readers are encouraged to refer to as they read. Fixing Your Damaged and Incorrect Genes is unique, there being no previously published books for non-biologists on the topic of DNA repair.

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Book SynopsisDNA profiling is often heralded as unassailable criminal evidence, a veritable "truth machine" that can overturn convictions based on eyewitness testimony, confessions, and other forms of forensic evidence. This book traces the controversial history of DNA fingerprinting by looking at court cases in the US and UK from the mid-1980s onwards.Trade Review"I could not put it down. This is a must-read for anyone interested in the history of science." (Times Higher Education) "An interesting read.... It illustrates that the controversy of DNA profiling is rooted not in the science, but mainly in the restrictions of the adversarial system." (Nature)"

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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 SynopsisGenome Transcriptome and Proteome Analysis is a concise introduction to the subject, successfully bringing together these three key areas of research. Starting with a revision of molecular genetics the book offers clear explanations of the tools and techniques widely used in genome, transcriptome and proteome analysis.Table of ContentsIntroduction. 1 General introduction. 1.1 Revision of molecular genetics. 1.2 The tools of molecular biology. 1.3 Specifics of the genome programmes. 1.4 The species analysed. 2 Linkage maps. 2.1. Tools and methods in genetic mapping. 2.2 The development of genetic maps. 2.3 Radiation hybrid maps. 2.4 Conclusion. 3 Physical maps. 3.1 Local maps or small genomes. 3.2 Strategies for physical mapping of the human genome. 3.3 Maps of the human genome. 3.4 Conclusion. 4 Genome sequencing . 4.1 Strategic choices. 4.2 Prokaryotic genomes. 4.3 Genomes of model eukaryotes. 4.4 The human genome. & lt;div> 4.5 Conclusion. 5 Sequencing cDNA and the transcriptome. 5.1 Strategies of cDNA sequencing. 5.2 The economic stakes. 5.3 The analysis of cDNA sequences. 5.4 The transcriptome. 5.5 Conclusion. 6 The proteome. 6.1 Basic techniques. 6.2 Transgenesis . 6.3 Mutagenesis. 6.4 Two-dimensional electrophoresis and identification of proteins. 6.5 Identification of protein interactions by twin hybrids. 6.6 Chip technology. 6.7 Analysis of three-dimensional structure. 6.8 Conclusions. 7 Identification of genes responsible for disease. 7.1 Genetic diseases. 7.2 Functional cloning and chromosomal anomalies. 7.3 Strategy for positional cloning. 7.4 What is the future for the cloning of disease-causing genes? 7.5 Conclusion. General conclusion. Bibliography.

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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 SynopsisDNA Transfer to Cultured Cells will describe the different updated methods and applications for transferring genetic material to cells in-vitro. Advances achieved in recent years to develop approaches for introducing DNA into cells have facilitated studies on the mechanism of expression of different important genes in the cells.Trade Review"..a very useful book." (Cell Biology International, Vol 24/7, 2000)Table of ContentsPartial table of contents: Production and Use of Retroviruses (G. Hicks, et al.). Electroporation of DNA into Cultured Cell Lines (L. Cataldo, et al.). Transfection of Rat Adipose Cells by Electroporation (M. Quon). Calcium Phosphate Transfection (K. Conn. et al.). Strontium Phosphate Transfection (R. Reddel, et al.). DEAE- Dextran Transfection (J. Schwartz & R. Rosenberg). Cationic Liposomes (V. Bichko). Transfection of Mammalian Cells with Yeast Artificial Chromosomes. (W. Strauss). Mapping Human Senescence Genes Using Microcell-Mediated Chromosome Transfer (R. Newbold & A. Cuthbert). Methotrexate and DHFR Amplification (M. Ricketts). List of Suppliers. Index.

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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 SynopsisIncludes a Foreword by Dr. James D. Watson, the co-discoverer of the DNA double helix, and Dr. Jan A. Witkowski. From the Foreword by Drs. Watson and Witkowski: ''DNA: Forensic and Legal Applications is a comprehensive and invaluable guide to the field, covering topics ranging from collecting samples in the field to presenting the complex results to a jury. We are sure that it will play its part in promoting this most powerful tool in the forensic scientist''s armamentarium.'' DNA: Forensic and Legal Applications covers the technology and laws related to DNA, as well as the use of DNA evidence in the legal system. This combination of science and law makes it the first comprehensive title of its kind and an appropriate reference for those with both elementary and advanced knowledge of the topic. It draws together in one source information that would previously have required extensive research and reliance on experts to obtain, offering both breadth and depth inTrade Review"There have been other forensic DNA books written before, but none has taken the reader through all aspects of DNA testing like this one...this book alone could make you feel prepared to understand a DNA case…" (Clinical Chemistry, September 2005) "…a comprehensive, up-to-date discussion of forensic DNA technologies and their legal implications in the courtroom." (CHOICE, April 2005) "…a concise guide to the use of DNA forensic analysis and how the information gained from such techniques are used in the legal system." (E-STREAMS, February 2005) This valuable codebook will go a long way toward helping us win the 'DNA battle'...we owe the authors a great debt of gratitude." (New York Law Journal, December 15, 2004)Table of ContentsForeword. Preface. Acknowledgements. 1. Biochemistry, Genetics, and Replication of DNA. 1.1 Evolution of Identification: From Faces to Fingerprints to DNA. 1.2 DNA and Heredity. 1.2.1 A Look at DNA from the Outside In. 1.2.2 DNA—The Chemistry. 1.2.3 Unique Sequence and Repetitious DNA. 1.3 DNA Replication. 1.3.1 Replication in the Cell. 1.3.2 Cloning (Gene Amplification). 2. Biological Evidence—Science and Criminal Investigation. 2.1 Crime Scene Investigation—Biological Evidence. 2.1.1 Help the Victim. 2.1.2 Protect the Scene. 2.1.3 Document the Scene. 2.1.4 Search the Scene. 2.1.5 Schematic Drawing Showing Location and Photography of Items of Evidence. 2.1.6 Packaging and Preserving Evidence. 2.1.7 Transport to Laboratory. 2.1.8 Sexual Assault Evidence. 2.1.9 Evidence Handling in the Laboratory. 2.1.10 Report Writing. 2.2 Serology. 2.2.1 Blood. 2.2.2 Semen. 2.2.3 Saliva. 2.2.4 Urine. 2.2.5 Hair. 2.3 Chain of Custody. 3. Forensic DNA Analysis Methods. 3.1 Associative Evidence and Polymorphism. 3.2 Restriction Fragment-Length Polymorphism. 3.2.1 Isolation of DNA. 3.2.2 Quantification. 3.2.3 Restriction Enzymes: DNA Scissors. 3.2.4 Gel Electrophoresis. 3.2.5 Southern Blotting. 3.2.6 Hybridization. 3.2.7 Autoradiography and Visualization of DNA Banding Pattern. 3.2.8 Analysis of RFLP Results. 3.2.9 Probe Stripping from Membrane. 3.2.10 Match Criteria. 3.2.11 Statistics and the Product Rule. 3.3 Polymerase Chain Reaction. 3.3.1 Development and Theory. 3.3.2 Isolation of DNA. 3.3.3 Quantification 3.3.4 Techniques. 3.4 Analysis of Y-Chromosome STRS. 3.4.1 Y-Chromosome Single-Nucleotide Polymorphism Analysis. 3.5 Analysis of Mitochondrial DNA. 3.5.1 The Mitochondrial Genome. 3.5.2 Quantification. 3.5.3 Sequencing. 3.5.4 Interpretation of Sequence Data. 3.5.5 Heteroplasmy. 3.5.6 Statistics. 3.5.7 SNP Analysis of Mitochondrial DNA. 3.6 Problems with PCR. 3.6.1 Contamination. 3.6.2 Degradation. 3.6.3 Sunlight. 3.6.4 Inhibitors. 3.6.5 Allelic Dropout—Null Alleles. 3.6.6 Human Error. 3.7 Underlying Facts and Assumptions in Forensic DNA Testing. 4. Genetics, Statistics, and Databases. 4.1 Human Genetics, Population Genetics, and Statistics. 4.1.1 Power of Forensic DNA Analysis: How Significant Is the Match? 4.1.2 Genetics and Statistics. 4.1.3 Mendel’s Laws of Genetics. 4.1.4 Meiosis. 4.2 Population Genetics. 4.2.1 Hardy–Weinberg Equilibrium. 4.2.2 Subpopulations and Substructure. 4.3 Need for Quality Control and Quality Assurance. 4.4 SWGDAM (Formerly Known as TWGDAM) Standards. 4.5 DNA Advisory Board. 4.6 Mitochondrial DNA and Y-Chromosome STR Analysis and Statistical Calculations. 4.7 Experimental Controls. 4.8 Validation of New DNA Methods. 4.9 Single-Nucleotide Polymorphism Analysis. 4.10 Database Size and Composition. 4.11 DNA Databases. 4.12 Power of Discrimination. 4.13 Mixtures and Statistics. 4.14 Probability of Exclusion. 4.15 Likelihood Ratio (LR). 4.16 Paternity Determinations. 4.16.1 Exclusion of the Alleged Father as the Biological Father. 4.16.2 Inclusion of the Alleged Father as the Biological Father. 4.17 Lab Accreditation, Certification, Reputation, and Facilities. 4.17.1 Quality Control. 4.17.2 Quality Assurance. 4.17.3 Proficiency Testing. 4.17.4 Certification. 4.17.5 Laboratory Accreditation. 4.18 Reviewing a DNA Report—A Sample RFLP Analysis. 4.19 Reviewing a DNA Report—A PCR-Based DNA Examination (HLADQA1, PM, D1S80, and CTT-CSF1PO, TPOX, THO1). 4.20 Reviewing a DNA Report—A PCR-STR-Based DNA Examination (CODIS Loci). 4.21 Reviewing a Paternity Report Based on Analysis of DNA. 5. Litigating a DNA Case. 5.1 Legal Theory. 5.1.1 Admissibility of Scientific Evidence: A Primer. 5.1.2 Common Law and The Creation of a Judicial Gatekeeping Function. 5.1.3 Federal Rules of Evidence and the Expansion of the Judicial Gatekeeping Function. 5.1.4 Daubert: The Supreme Court Sets Forth a Standard. 5.1.5 General Electric Company et al. v. Joiner et ux. 5.1.6 Kumho Tire: The Court Continues Its Expansion of the Judicial Gatekeeping Function. 5.1.7 Judicial Gatekeeping Function and Its Evolution in New York State. 5.2 Admissibility of DNA Evidence. 5.2.1 PCR-STR DNA Evidence. 5.2.2 Mitochondrial DNA. 5.2.3 Animal DNA. 5.2.4 Plant and Viral DNA. 5.2.5 Statistics. 5.2.6 Paternity. 5.3 Legal Practice. 5.3.1 Different Stages of a Trial. 5.4 DNA for Defense Attorneys—Contesting DNA Evidence. 5.5 DNA for Prosecutors. 5.6 DNA for Judges. 6. DNA Evidence at Trial. 6.1 Attacking and Defending DNA Evidence. 6.1.1 Theory of the Case/Plan of Attack. 6.1.2 What is Required for DNA Test Results to be Admitted into Evidence? 6.2 DNA for the Prosecutor or Those Who Seek to Admit DNA Evidence. 6.2.1 Effective Admission of DNA Evidence Takes Place in Three Stages. 6.3 DNA for the Defense or Those Who Seek to Mitigate the Effect of DNA Evidence. 6.3.1 Preventing the Admission of DNA Evidence in Part or in Its Entirety. 7. Exonerating the Innocent through DNA. 7.1 Postconviction Appeals Based upon DNA Evidence. 7.2 Postconviction DNA Testing: Recommendations for Handling Requests. 7.2.1 Role and Response of the Prosecutor. 7.2.2 Role and Response of the Defense Attorney. 7.3 Legal Standards Governing Postconviction Testing. 7.3.1 Argument for a Constitutional Right to Postconviction DNA Testing. 7.3.2 Other Non-Postconviction Testing Statute Arguments. 7.4 Postconviction DNA Testing Statutes. 7.5 Preventing Postconviction DNA Testing through Waiver. 7.6 The Future of DNA Technology. Appendix A: Bibliography: Selected by Topic Area. Appendix B: Cases Involving the Admissibility of DNA Evidence. Appendix C: Information Pertinent to Attempts to Overturn Convictions Based Upon DNA Evidence. Appendix D: Offenses in New York State Resulting in Mandatory DNA Testing for Database Inclusion. Appendix E: Postconviction DNA Testing, Preservation of Evidence and Compensation for Wrongful Convictions: Relevant Legislative Information. Appendix F: Items Obtained through Discovery. Appendix G: Glossary. Index.

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Book SynopsisThe DNA molecule, which has been traditionally studied by techniques developed through molecular biology, is considered here rather from a physicist's viewpoint, as a nonlinear dynamical system.Table of ContentsDNA Structure. DNA Dynamics. DNA Functioning. Linear Theory of DNA. Nonlinear Theory of DNA: Ideal Dynamical Models. Nonlinear Theory of DNA: Non-Ideal Models. Nonlinear Theory of DNA: Statistics of Nonlinear Excitations. Experimental Tests of DNA Nonlinearity. Nonlinearity and Functioning. Appendices. References. Index.

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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 SynopsisA majority of evolutionary biologists believe that we now can envision our biological predecessors - not the first, but nearly the first, living beings on Earth. This title is about these vanished forebears, sketching them in the distant past just as their workings first began to resemble our own.Trade ReviewLife from an RNA World is an unconventional book about RNA. Rather than opening with the central dogma and attendant teachings on molecular biology, Yarus uses evolution as a gateway. He then takes us on a journey through evolutionary time, concentrating on the roles of the various forms of RNA… [He] is a proficient guide. -- Tim Harris * Nature *Although precise historical details of the particular origin of life on Earth are probably unknowable, most scientists agree that a world existed in which RNA performed the duties of both genes and enzymes. This RNA world in turn evolved into the DNA–RNA–protein world of today. Michael Yarus’s Life from an RNA World offers an engaging introduction to the subject… Recent discoveries make Yarus’s book particularly timely, especially as a light-hearted introduction for scientifically minded readers outside the field. His chatty prose conveys the voice of a tour guide on a journey through the RNA world, introducing essential evolutionary and molecular biology and pointing out must-not-miss attractions. Even members of the origins-of-life community may appreciate his whimsical explanations of familiar phenomena. -- Irene A. Chen * Science *Michael Yarus’ book is a very enjoyable read, be the reader a well informed molecular biologist, or a lay person… Surely this book will highlight and increase the interest in the RNA world; raising the awareness that we are all, after all, the children of RNA. -- Michael Ladomery * Chemistry World *Yarus captivates with skilled character development—but here, the ‘characters’ are the prebiotic molecules that gave rise to everything that has ever lived or is alive today on our planet. -- Thomas Cech, Distinguished Professor, University of Colorado–Boulder, and 1989 Nobel Laureate in ChemistryTable of Contents* Introduction to Your Ancestor * Before We Begin: A Voluntary Chapter * Framing the Problem: The Buffalo and the Bacterium * The Big Tree: No Jackalopes Please * A Dance of Atoms * Allegro Agitato: The Origin of Life * The Winds That Blow through the Starry Ways * Tornados in a Junkyard * Between Genomes and Creatures * A Thumbnail Molecular Biology * RNA Structure: A Tape with a Shape * Intimations of an RNA World * The Experimentally Impaired Sciences * Test Tube RNA Evolution: First Light * Selection Amplification: Interrogating RNA's Possibilities * RNA Duplication: Replicase Activity in Real RNAs * RNA Capabilities and the Origins of Translation * The Quest for the Peptidyl Transferase * A Language Much Older Than Hieroglyphics: The Genetic Code * Assume a Spherical Cow: The Ribocyte * The Future of the RNA World * Lexicon * Index

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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 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 SynopsisGenomics and genome technology is having, and continues to have, a major impact on all areas of bioscience research providing insights into the key area of molecular mechanisms of cells in health and disease. This is causing a profound effect on biomedical science and is accelerating the development of new diagnostic applications. This book provides a timely, graduate level introduction to the fast-paced area of genomics and clinical diagnostic technologies and introduces the concept of applications based on this area. The initial chapters focus on principal molecular technologies that underpin the information in the later chapters. In addition to introductory areas of nucleic acids and techniques in molecular biology, bioinformatics and proteomics, other key diagnostic areas such as the use of immunological reagents are covered. The later chapters provide more specialised examples of currently used diagnostic technologies and insights into selected key diagnostic challenges including specific examples of molecular microbial diagnostics and molecular biomarkers in oncology. The running themes through the chapters provides an insight into current and future perspectives in this rapidly evolving field.Table of ContentsNucleic Acid Structure and Basic Analysis; Introduction to Genomic Diagnostics; Molecular Cloning and DNA Sequencing; Microbial Proteomics; Introductory Bioinformatics; Genomics and Diagnostics in Acute Myeloid Leukaemia; Comparative Genomics and the Improved Diagnosis of Trypanosomiasis and Visceral Leishmaniasis; Rare Disease Genomics and Clinical Diagnostics; Molecular Diagnostics; Nanomaterials in Human Disease Diagnosis; Molecular Microbial Diagnostics; Parasite Genomes and Diagnostics; Connective Tissue Disorders - Ehlers-Danlos Syndrome; Antibody-based Diagnostics; Molecular Biomarkers: Overview, Technologies and Strategies; Next-Generation Molecular Markers: Challenges, Applications and Future Perspectives; Genomics for Clinical Practice in Oncology; Subject Index

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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 SynopsisTechnology for modifying the genotypes and phenotypes of insects and other arthropods has steadily progressed with the development of more precise and powerful methods, most prominently transgenic modification. For many insect pests, there is now almost unlimited ability to modify phenotypes to benefit human health and agriculture. Precise DNA modifications and gene drive have the power to make wild-type populations less harmful in ways that could never have been performed with previous transgenic approaches. This transition from primarily laboratory science to greater application for field use has also necessitated greater development of modeling, ethical considerations and regulatory oversight. The 2nd Edition of Transgenic Insects contains chapters contributed by experts in the field that cover technologies and applications that are now possible. This edition includes increased attention to associated challenges of risk assessment, regulation, and public engagement. Featuring: Up-to-date analysis of molecular techniques, such as gene editing. Consideration of public attitudes and regulatory aspects associated with transgenic insects. Many examples of the wide range of applications of transgenic insects. This book will be very valuable to students and researchers in entomology, molecular biology, genetics, public health and agriculture, and will also appeal to practitioners who are implementing the technology, and to regulators, stakeholders and ethicists.Table of ContentsChapter 1: Transposon-based Technologies for Insects Chapter 2: Inducible and Repressible Systems for Transgene Expression Chapter 3: Sex-, Tissue- and Stage-Specific Transgene Expression Chapter 4: RNA Interference to Modify Phenotypes in Agriculturally Important Pest and Beneficial Insects: Useful Examples and Future Challenges Chapter 5: Site-specific Recombination for Gene Locus-directed Transgene Integration and Modification Chapter 6: Receptor-Mediated Ovary Transduction of Cargo - ReMOT Control: A Comprehensive Review and Detailed Protocol for Implementation Chapter 7: Site-directed DNA Sequence Modification Using CRISPR-Cas9 Chapter 8: An Introduction to the Molecular Genetics of Gene Drives and Thoughts on Their Gradual Transition to Field Use Chapter 9: Drosophila melanogaster As a Model for Gene Drive Systems Chapter 10: Sex Ratio Manipulation Using Gene Drive for Mosquito Population Control Chapter 11: Population Modification Using Gene Drive for Reduction of Malaria Transmission Chapter 12: Modelling threshold-dependent gene drives Chapter 13: Tsetse Paratransgenesis: a Novel Strategy for Reducing the Spread of African Trypanosomiases Chapter 14: Paratransgenic Control of Chagas Disease Chapter 15: Asaia Paratrangenesis in Mosquitoes Chapter 16: Paratransgenesis in Mosquitoes and Other Insects: Microbial Ecology and Bacterial Genetic Considerations Chapter 17: Transgenic approaches for sterile insect control of dipteran livestock pests and lepidopteran crop pests Chapter 18: Honey bee genome editing Chapter 19: Progress Towards Germline Transformation of Ticks Chapter 20: Silkworm Transgenesis and its Applications Chapter 21: Tephritid Fruit Fly Transgenesis and Applications Chapter 22: Antiviral Effectors for Mosquito Transgenesis Chapter 23: Self-Limiting Insects for Pest Management Chapter 24: Public Acceptability and Stakeholder Engagement for Genetic Control Technologies Chapter 25: Regulation of Transgenic Insects Chapter 26: Economics of Transgenic Insects for Field Release Chapter 27: The Cartagena Protocol on Biosafety and the Regulation of Transboundary Movement of Living Modified Organisms Chapter 28: Risk Analysis of Transgenic Insects

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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 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 two-volume work focuses on the mathematical aspects of Darwinian evolution starting from the basic model of stochastic evolution of a single isolated locus in the presence of mutation to the multi–locus models of sexual and asexual populations. Volume 2 discusses the inference of fi tness landscape from DNA sequence data, discovery of the evolutionary roles of enygmatic traits, co-evolution of adversarial species, and various applications to virus evolution.

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Book SynopsisThis book delves into various applications and tools of bioinformatics in a range of fields, including agriculture, corals, structural bioinformatics, data mining, text mining, medicinal plants, antibiotics, protein structure prediction, drug design, gene expression, micro-arrays, proteomics, molecular phylogenetics, artificial neural networks, plant systems biology, molecular modeling, homology modeling, bio-efficacy of indigenous bacillus through in-silico approach, and fresh aquaculture and fisheries. It also covers topics such as pesticides and insecticides, database and tool development in relevant areas, and would be of great utility to individuals working in the fields of agricultural biotechnology, bioinformatics, computer science, and applied statistics.

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Book SynopsisThis book focuses on a variety of bioinformatics tools and applications across several fields, including agriculture, corals, structural bioinformatics, data and text mining, medicinal plants, antibiotics, protein structure prediction, drug design, gene expression, microarrays, proteomics, molecular phylogenetics, rough sets for protein structural class prediction, artificial neural networks for predicting amino acid levels, plant systems biology, molecular modeling, homology modeling, bioefficacy of indigenous bacillus through in silico approach, and fresh aquaculture and fisheries. This book would be of value to individuals working in agricultural biotechnology, bioinformatics, computer science, and applied statistics. Rephrased with a formal tone.

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Book SynopsisAgro-informatics is the application in agriculture with innovative ideas, techniques and scientific knowledge to expand the horizons of the Computer Science. The book contains the details about the information technology applied to management and analysis of agricultural data The book covers the diverse areas ranging from artificial intelligence, artificial neural networks, decision support system, expert system, geographic information system, information system related to agriculture, genetic algorithm, programming language with backend tool to develop softwares The book contains fifteen s that highlight and discuss the various dimensions of agro-informatics. It is hoped that the book will provide the basic and fundamental knowledge of understanding the concepts of Bioinformatics. This book is prepared, taking into consideration the changing needs of the undergraduate curricula of various universities involved in offering courses on agro-informatics The book will also be a guide for the researchers The book would also provide the required computer knowledge to the students of State Agricultural Universities and other ICAR Institutions to sharpen their understanding of agro-informatics.Table of Contents01. Introduction to Agroinformatics 02. Agriculture Information System 03. Farm Management System 04. Management Information System 05. Geographic Information System 06. Artificial Intelligence 07. Artificial Neural Networks 08. Expert Systems 09. Decision Support System DSS 10. Genetic Algorithms 11. Programming Language - Visual Basic 12. Database Management System 13 . Agricultural Databases, Technical Report and Conference Reports 14. Web Portal 15. Introduction to Bioinformatics

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Book SynopsisThe book deals with various tools and applications of bioinformatics in the fields of: o agriculture, corals, structural bioinformatics, data-mining, text-mining; o medicinal plants, antibiotics, protein structure prediction, drug design; o gene expression, micro-arrays, proteomics, molecular phylogenic location of the Indian Liver Fluke, rough sets to predict protein structural class; o artificial neural networks for prediction of amino acids levels, plant systems biology, molecular modeling, homology modeling, bio-efficacy of indigenous bacillus through in-silico approach; o fresh aquaculture and fisheries, pesticides and insecticides, databases and tools development in the relevant area. The book would be of much use to the person working in the field of agricultural biotechnology, bioinformatics, computer science and applied statistics. This can act as a book for M.Sc, M.Tech and Ph.D students of and the faculty of Bioinformatics/Biotechnologists.Table of Contents01. Application of Bioinformatics in Agriculture D. Velmurugan 02. Statistical Data Mining-Concepts, Tools and Techniques for Bioinformatics Ravi R. Saxena, Ritu R. Saxena, Akash Solanki and M.L. Lakhera 03. Prediction of 3D Structure of Proteins Using Artificial Neural Networks K. Meena 04. A Genomic Approach to Study the Insect Mitochondrial Evolution by Noncoding LSU and SSU rRNA 41S. Gifta Lorraine, Christy Jancy Joy P., Arul Samraj D. and Priyakumari Joyce C. 05. Analysis of Microarray Gene Expression Data M. Balakrishnan, R.C. Srivastava and M. Ramachandran 06. A Novel Sequence Encoding Method for Searching Rareness Among Consensus DNA Sequences K. Meena, K. Menaka, T.V. Sundar and K. R. Subramanian 07. Statistical Methods for Genomic Sequence and Microarray Analysis A.K. Roy and S.R. Martha 08. Significance of Insect Genomics in Agro Ecosystems R.W. Alexander Jesudasan09. Bioinformatics Tools in Fish Molecular Modeling Grinson George, K.Sarma, S. Dam Roy and Mukunda Goswamy10. Structural Bioinformatics Goals and Challenges Mayank Pokhriyal and M. Balakrishnan 11. Database for Animal Genetic Resources of A &N IslandsM. Balakrishnan, S. Jeyakumar, R.C. Srivastava, Mayank Pokhriyal and Soma Mondal 12. A Database for Medicinal Plants used in Treatment of Asthma K. Balaji, M. Rajadurai, G. Vidhya, A. Krithika, E. Hudson, Vidya Murali, M. Ramya, Anusha Bhaskar and V. Stanley 13. In-silico Determination of Potential Drug Target for Curing Meningococcal Disease- Bioinformatics Study On Penicillin- Binding Proteins Vijay Tripathi, Dwijendra Gupta and Navita Srivastava 14. Text Mining: Potential Applications in Bioinformatics M. Balakrishnan, R.C. Srivastava, S.K. Zamir Ahmed and Subhash Chand 15. Diversity and Antibacterial Activity of Coral Associated Actinomycetes P. Nithyanand, S. Manju and S. Karutha Pandian 16. Decision Tree and its Application in Bioinformatics Rajni Jain and Amarendra Kumar Mishra 17. Database for Algae Resources in Andaman and Nicobar Islands M. Balakrishnan 18. Bioinformatics of Natural Antibiotics S. Rajarajan, M. Yogambal, K. Sangeetha 19. PhytopathoSys v1.0 A Phytopathology Information System M. Balakrishnan, R.C. Srivastava, Michael Imanual Jesse and M. Ramachandran 20. Multiple Gene Expression Profiles of Chemically Induced Parkinson Disease: Implication in Neuroprotection Mechanism Offered by Nicotine and Caffeine Kavita Singh, M.P. Singh and M. Balakrishnan 21. Development of Databases on Freshwater Aquaculture A.K. Roy and Nibedita Jena 22. Proteomics and Bioinformatics A. Subash Chandira Bose, R. Venkateshwaran and M. Balakrishnan23. Data Mining and its Applications in Bioinformatics Soma Mondal and M. Balakrishnan 24. Molecular Phylogenic Location of the Indian Liver Fluke, Fasciola (Trematoda: Fasciolidae) Based on The Ribosomal Internal Transcribed Spacer Regions P. K. Prasad, V. Tandon, D.K. Biswal, L.M. Goswami and A. Chatterjee 25. Prediction of Amino Acid Levels in Protein Sequences Using Artificial Neural Networks K.Meena and M. Manimekalai 26. Applying New Data Mining Method of Rough Sets to Predict Protein Structural Class K. Meena and M. Durairaj 27. Plant Systems Biology G. Ramesh Kumar, CP Rajadurai and M. Ramachandran 28. Bio-efficacy of Indigenous Bacillus thuringiensis LDC-9 Against Insect Pest and its Characterization through In-silico Approach R. Shenbagarathai and A. Mahalakshmi 29. Molluscan Resources and Its Management Measures in Andaman & Nicobar Islands S.N. Sethi, M. Balakrishnan, S. Chand and B.Varghese

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Book SynopsisEnvironmental DNA (eDNA) refers to DNA that can be extracted from environmental samples (such as soil, water, feces, or air) without the prior isolation of any target organism. The analysis of environmental DNA has the potential of providing high-throughput information on taxa and functional genes in a given environment, and is easily amenable to the study of both aquatic and terrestrial ecosystems. It can provide an understanding of past or present biological communities as well as their trophic relationships, and can thus offer useful insights into ecosystem functioning. There is now a rapidly-growing interest amongst biologists in applying analysis of environmental DNA to their own research. However, good practices and protocols dealing with environmental DNA are currently widely dispersed across numerous papers, with many of them presenting only preliminary results and using a diversity of methods. In this context, the principal objective of this practical handbook is to provide biTrade ReviewThis volume fills a much-needed gap, offering a gentle introduction into the field of environmental DNA, which will be especially useful for readers of minor to intermediate experience with environmental DNA. * Vasco Elbrecht, Centre for Biodiversity Genomics, University of Guelph, The Quarterly Review of Biology *An excellent instructional book or supplementary reading for any eDNA based classes...It is a timely and important addition to the field of molecular ecology, and will undoubtedly remain the go-to book on metabarcoding for several years. * Dr Anthony A. Charlton, Macquarie University, Sydney, Australia, Molecular Ecology *This book is a timely overview of eDNA as a complimentary and non-invasive approach for investigating and monitoring biodiversity. The book is an ideal introduction to all ecologists looking to eDNA, but also speaks to the more experienced researchers in molecular ecology. Lastly, it provides textbook material for university courses around the world. * Philip Francis Thomsen, Trends in Ecology & Evolution Journal *If you are contemplating moving into this topic, or just want to understand it better, do try and get your hands on a copy - something that might not be that easy just now as I understand the book has proved to be so popular that it is already having to be reprinted. * IMA FUNGUS *In a world faced with accelerating environmental change and loss of biodiversity, this book is a timely overview of eDNA as a complementary and noninvasive approach for investigating and monitoring biodiversity ... an ideal introduction to all ecologists looking to eDNA as a method of choice, but also speaks to the more experienced researchers in molecular ecology. Lastly, it provides textbook material for university courses around the world, where eDNA is continuously increasing in popularity. * Philip Francis Thomsen, Department of Bioscience, University of Aarhus, Trends in Ecology and Evolution *Table of Contents1: Introduction to environmental DNA (eDNA) 2: DNA metabarcode choice and design 3: Reference databases 4: Sampling 5: DNA extraction 6: DNA amplification and multiplexing 7: DNA sequencing 8: DNA metabarcoding data analysis 9: Single-species detection 10: Environmental DNA for functional diversity 11: Some early landmark studies 12: Freshwater ecosystems 13: Marine environments 14: Terrestrial ecosystems 15: Palaeoenvironments 16: Host-associated microbiota 17: Diet analysis 18: Analysis of bulk samples 19: The future of eDNA metabarcoding

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Book SynopsisIn 1957 two young scientists produced an experiment confirming that DNA replicates as predicted by the double helix structure Watson and Cricks had proposed. This text reconstructs the complex route that led to the experiment and provides an inside view of day-to-day scientific research.Trade Review"Holmes has written a remarkable history of one of molecular biology's most significant experiments. His recreation of a seminal time in the development of the field is masterful and detailed, and it is exciting reading for anyone interested in how experimentation is actually done in a molecular biology laboratory." David Baltimore, president, California Institute of Technology

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Book SynopsisDevoted to local and global analysis of weakly connected systems with applications to neurosciences, this book uses bifurcation theory and canonical models as the major tools of analysis.Trade ReviewFrom the reviews: "...After the introduction, written according to the authors in ordinary language, and well readable even for laymen, follows a nicely written Chapter 2 on bifurcations in neuron dynamics which must be read. Here also spiking and bursting phenomena are clearly described. Chapter 3 contains a short sketch of nonhyperbolic (when the Jacobian matrix of (1) has at least one eigenvalue with zero real part) neural networks. The remaining part of the book is mainly devoted to canonical models (Chapter 4), their derivation (Chapters 6--9), and their analysis (Chapters 10--12). The term canonical model is not precisely defined here. The authors say that a model is canonical if there is a continuous change of variables that transforms any other model from a given class into this one. As the method of deriving the canonical models, the authors exploit the normal form theory. Canonical models treated in the book have only restricted value: They provide information about local behavior of (1) when there is an exponentially stable limit cycle but they say nothing about global behavior of (1), including the transients. The last Chapter 13 describes the relationship between synaptic organizations and dynamical properties of networks of neural oscillators. In other words, the problem of learning and memorization of phase information in the weakly connected network of oscillators corresponding to multiple Andronov-Hopf bifurcation is treated analytically. Surprisingly the book ends without any conclusions. Also there are no appendices to the book. The references are representative and sufficiently cover the problematics treated in the book." (Ladislav Andrey, Mathematical Reviews) Table of Contents1 Introduction.- 2 Bifurcations in Neuron Dynamics.- 3 Neural Networks.- 4 Introduction to Canonical Models.- 5 Local Analysis of WCNNs.- 6 Local Analysis of Singularly Perturbed WCNNs.- 7 Local Analysis of Weakly Connected Maps.- 8 Saddle-Node on a Limit Cycle.- 9 Weakly Connected Oscillators.- 10 Multiple Andronov-Hopf Bifurcation.- 11 Multiple Cusp Bifurcation.- 12 Quasi-Static Bifurcations.- 13 Synaptic Organizations of the Brain.- References.

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Book SynopsisThis book is the first to approach the fast developing field of wildlife forensics with a focus on the application of DNA profiling and analysis. Case studies throughout link theory and practice and highlight the use of DNA testing in species testing.Table of ContentsForeword ix Preface xi About the Authors xiii Acknowledgements xv 1 Introduction 1 1.1 Importance of wildlife forensic science investigations 1 1.2 Role of forensic science in wildlife crimes 3 1.3 Legislation covering wildlife crime 4 1.4 Role of non-human DNA in forensic science 8 1.5 Development of wildlife DNA testing 9 1.5.1 History and current state of wildlife DNA forensic science 10 1.5.2 Wildlife forensic science testing 11 1.5.3 Performing DNA typing in wildlife investigations 13 1.6 Accreditation and certification 14 1.7 Standardisation and validation 20 1.8 Collection of evidential material, continuity of evidence and transportation to the laboratory 24 1.9 Note taking and maintenance of a casefile 29 1.10 Case assessment and initial testing 30 1.11 Scope of book 32 Useful websites 32 References 33 2 DNA, Genomes and Genetic Variation 37 2.1 Introduction 37 2.2 The DNA molecule 37 2.3 Chromosomes and nuclear DNA 39 2.4 Genomes 41 2.4.1 Nuclear DNA 41 2.4.2 Mitochondrial and chloroplast DNA 44 2.5 DNA mutation and genetic variation 47 2.5.1 Genetic variation of repetitive DNA 48 2.5.2 Single base changes leading to genetic variation 48 2.5.3 Genetic loci used in species testing 50 2.6 DNA polymorphisms leading to speciation 53 2.6.1 Genetic isolation 54 2.6.2 Other processes leading to speciation 56 2.7 What is a species? 56 2.7.1 Subspecies 60 2.7.2 Genus to Kingdom 61 2.8 Summary 63 References 64 3 Methods in Wildlife Forensic DNA Analysis 69 3.1 Introduction 69 3.2 Protein polymorphisms 69 3.3 DNA isolation, purification and concentration 70 3.3.1 Generic aspects of DNA isolation 70 3.3.2 Lysis step 71 3.3.3 DNA purification: silica-based extraction 72 3.3.4 DNA purification: Chelex R 100 resin 73 3.3.5 DNA purification: organic extraction 74 3.3.6 Microconcentration 76 3.4 DNA quantification 76 3.5 Restriction fragment length polymorphisms (RFLP) 78 3.6 Methods based on the polymerase chain reaction 81 3.6.1 Factors affecting PCR efficiency and optimisation of PCR 84 3.6.2 PCR-based methods of DNA quantification 88 3.6.3 Random amplification of polymorphic DNA 91 3.6.4 Amplification of fragment length polymorphisms (AFLP) 93 3.7 PCR set-up 95 3.8 PCR clean-up 98 3.9 DNA sequencing 99 3.10 SNP typing 100 3.11 New generation of DNA sequence methods 102 Suggested reading 104 4 Species Testing 105 4.1 Introduction 105 4.2 Species 106 4.2.1 Genetic variation and correspondence with taxonomy 106 4.3 Attributes of a species testing locus 106 4.4 Application of a locus to a species 110 4.5 Tests available and how they are performed 110 4.5.1 Sequencing 111 4.5.2 Species-specific primers 124 4.6 Developing a species test 127 4.6.1 Use of data on GenBank and sequence alignment 128 4.6.2 Designing primers 135 4.6.3 Validation 156 4.7 Interpretation and reporting of results 159 4.7.1 Interpretation and reporting sequencing results 160 4.7.2 Interpretation and reporting species-specific testing results 169 4.8 Other limitations: hybrids and wild/captive bred 171 4.9 Future methodologies 173 References 173 5 Genetic Linkage 177 5.1 Introduction 177 5.2 Whole genome testing 177 5.3 Types of individualisation testing 178 5.3.1 Short Tandem Repeats 179 5.4 Identifying STR loci 182 5.4.1 DNA libraries 183 5.4.2 Locating novel microsatellite motifs using Next Generation Sequencing 184 5.5 Allele databases 190 5.5.1 Number of theoretical genotypes 192 5.5.2 Allelic ladders 192 5.6 Hardy–Weinberg equilibrium 193 5.7 Kinship factors and accounting for shared alleles 199 5.7.1 Rare or absent alleles on the database 202 5.8 Assessing the suitability of STR loci 203 5.8.1 The Genetic Data Analysis software (GDA) 205 5.8.2 The Excel Microsatellite Toolkit 214 5.8.3 Arlequin 220 5.8.4 API-Calc 228 5.8.5 Genepop 230 5.8.6 FSTAT 235 5.8.7 Structure 236 5.8.8 Summary 242 5.9 Genetic assignment: paternity testing 244 5.9.1 Genetic assignment: paternity testing if one parent is not available 249 5.9.2 Genetic assignment in paternity testing, incorporating kinship factor 251 5.10 Concluding comments 253 References 254 6 Interpretation, Evaluation and Reporting of Results 259 6.1 Introduction 259 6.2 Case assessment 260 6.3 Hierarchies of propositions 261 6.4 DNA evidence evaluation 262 6.4.1 The frequentist approach 263 6.4.2 Likelihood ratios 264 6.4.3 The Bayesian approach 266 6.4.4 Comparison of the three approaches 267 6.5 Evaluation of DNA evidence in wildlife cases 269 6.5.1 Case scenario 1 269 6.5.2 Case scenario 2 271 6.5.3 Case scenario 3 272 6.6 Role of the expert witness 273 6.7 Report writing 275 6.8 Summary and comments 277 Statement of witness 278 References 299 Measurements 303 Glossary 305 Appendix A Simulated Sample Populations 311 Appendix B Useful websites 323 Index 325

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Book SynopsisWhat are the evolutionary mechanisms and ecological implications behind a pollinator choosing its favourite flower? Sixty-five million years of evolution has created the complex and integrated system which we see today and understanding the interactions involved is key to environmental sustainability. Examining pollination relationships from an evolutionary perspective, this book covers both botanical and zoological aspects. It addresses the puzzling question of co-speciation and co-evolution and the complexity of the relationships between plant and pollinator, the development of which is examined through the fossil record. Additional chapters are dedicated to the evolution of floral displays and signalling, as well as their role in pollination syndromes and the building of pollination networks. Wide-ranging in its coverage, it outlines current knowledge and complex emerging topics, demonstrating how advances in research methods are applied to pollination biology.Table of ContentsPreface; 1. Macroevolution for plant reproductive biologists Paul Wilson; 2. Pollination crisis, plant sex systems and predicting evolutionary trends in attractiveness Tom J. de Jong; 3. Evolution and ecological implications of 'specialized' pollinator rewards W. Scott Armbruster; 4. Fig-fig wasp mutualism, the fall of the strict co-speciation paradigm? Astrid Cruaud, James Cook, Yang Da-Rong, Gwenaëlle Genson, Roula Jabbour-Zahab, Finn Kjellberg, Rodrigo Augusto Santinelo Pereira, Nina Rønsted, Otilene Santos-Mattos, Vincent Savolainen, Rosichon Ubaidillah, Simon van Noort, Peng Yan-Qiong and Jean-Yves Rasplus; 5. Fossil bees and their plant associates Denis Michez, Maryse Vanderplanck and Michael S. Engel; 6. Pollen evidence for the pollination biology of early flowering plants Shusheng Hu, David L. Dilcher and David Winship Taylor; 7. Pollinator mediated floral divergence in the absence of pollinator shifts Allan G. Ellis and Bruce Anderson; 8. Animal pollination and speciation in plants: general mechanisms and examples from the orchids Florian P. Schiestl; 9. Why are floral signals complex? An outline of functional hypotheses Anne S. Leonard, Anna Dornhaus and Daniel R. Papaj; 10. A survey on pollination modes in cacti and a potential key innovation Boris O. Schlumpberger; 11. Zygomorphy, area and the latitudinal biodiversity gradient in angiosperms Jana C. Vamosi and Steven M. Vamosi; 12. Ambophily and 'super generalism' in Ceratonia siliqua (Fabaceae) pollination Amots Dafni, Talya Marom-Levy, Andreas Jürgens, Stefan Dötterl, Yuval Shimrat, Achik Dorchin, H. Elizabeth Kirkpatrick and Taina Witt; 13. Structure and dynamics of pollination networks: the past, the present and the future Jens M. Olesen, Yoko L. Dupont, Melanie Hagen, Claus Rasmussen and Kristian Trøjelsgaard; 14. Pollinators as drivers of plant distribution and assemblage into communities Loïc Pellissier, Nadir Alvarez and Antoine Guisan; 15. Effects of alien species on plant-pollinator interactions: how can native plants adapt to changing pollination regimes? Gideon Pisanty and Yael Mandelik; 16. Pollen resources of non-Apis bees in southern Africa Michael Kuhlmann and Connal D. Eardley; 17. Advances in the study of the evolution of plant-pollinator relationships Sébastien Patiny; Index.

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Book SynopsisProbabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.Trade Review'This book fills an important gap in the bioinformatics literature and should be required reading for anyone who is interested in doing serious work in biological sequence analysis. For biologists who have little formal training in statistics or probability, it is a long-awaited contribution that, short of consulting a professional statistician who is well versed in molecular biology, is the best source of statistical information that is relevant to sequence-alignment problems. This book seems destined to become a classic. I highly recommend it.' Andrew F. Neuwald, Trends in Biochemical Sciences'This book is a nice tutorial and introduction to the field and can certainly be recommended to all who wish to analyse biological sequences with computer methods. It can also serve as a basis for a university course for undergraduates.' Trends in Cell Biology' … an enjoyable opportunity to see a blend of modeling and data analysis at work on an important class of problems in the rapidly growing field of computational biology.' D. Siegmund, Short Book ReviewsTable of Contents1. Introduction; 2. Pairwise sequence alignment; 3. Multiple alignments; 4. Hidden Markov models; 5. Hidden Markov models applied to biological sequences; 6. The Chomsky hierarchy of formal grammars; 7. RNA and stochastic context-free grammars; 8. Phylogenetic trees; 9. Phylogeny and alignment; Index.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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