Description

Book Synopsis

Orientation-Aware Graph Neural Networks for Protein Structure Representation Learning.- Active Learning for Protein Structure Prediction.- Sequence-based TCR-Peptide Representations Using Cross-Epitope Contrastive Fine-tuning of Protein Language Models.- DualGOFiller: A Dual-Channel Graph Neural Network with Contrastive Learning for Enhancing Function Prediction in Partially Annotated Proteins.- Detecting antimicrobial resistance through MALDI-TOF mass spectrometry with statistical guarantees using conformal prediction.- Hierarchical Spatio-Temporal State-Space Modeling for fMRI Analysis.- A Phylogenetic Approach to Genomic Language Modeling.- Dynamic Programming Algorithms for Fast and Accurate Cell Lineage Tree Reconstruction from CRISPR-based Lineage Tracing Data.- Old dog, new tricks: Exact seeding strategy improves RNA design performances.- Scalable and Interpretable Identification of Minimal Undesignable RNA Structure Motifs with Rotational Invariance.- An Exact and Fast SAT Formulation for the DCJ Distance.- Improved pangenomic classification accuracy with chain statistics.- Dynamic μ-PBWT: Dynamic Run-length Compressed PBWT for Biobank Scale Data.- Prokrustean Graph: A substring index for rapid k-mer size analysis.- Rag2Mol: Structure-based drug design based on Retrieval Augmented Generation.- Rewiring protein sequence and structure generative models to enhance protein stability prediction.- Learning a CoNCISE language for small molecule binding and function.- An adversarial scheme for integrating multi-modal data on protein function.- Decoding the Functional Interactome of Non-Model Organisms with PHILHARMONIC.- The tree labeling polytope: a unified approach to ancestral reconstruction problems.- ScisTree2: An Improved Method for Large-scale Inference of Cell Lineage Trees and Genotype Calling from Noisy Single Cell Data.- OMKar: optical map based automated karyotyping of genomes to identify constitutional disorders.- TarDis: Achieving Robust and Structured Disentanglement of Multiple Covariates.- devider: long-read reconstruction of many diverse haplotypes.- Pharming: Joint Clonal Tree Reconstruction of SNV and CNA Evolution from Single-cell DNA Sequencing of Tumors.- GEM-Finder: dissecting GWAS variants via long-range interacting cis-regulatory elements with differentiation-specific genes.- Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states.- cfDecon: Accurate and interpretable methylation based cell type deconvolution for cell-free DNA.- Inferring cell differentiation maps from lineage tracing data.- Alignment-free estimation of read to genome distances and its applications.- ML-MAGES: A machine learning framework for multivariate genetic association analyses with genes and effect size shrinkage.- TX-Phase: Secure Phasing of Private Genomes in a Trusted Execution Environment.- Hyper-k-mers: efficient streaming k-mers representation.- Characterizing the Solution Space of Migration Histories of Metastatic Cancers with MACH2.- Causal Disentanglement of Treatment Effects in Single-cell RNA Sequencing through Counterfactual Inference.- Integration and querying of multimodal single-cell data with PoE-VAE.- ralphi: a deep reinforcement learning framework for haplotype assembly.- GeneCover: A Combinatorial Approach for Label-free Marker Gene Selection.- Joint imputation and deconvolution of gene expression across spatial transcriptomics platforms.- ScatTR: Estimating the Size of Long Tandem Repeat Expansions from Short-Reads.- Learning Latent Trajectories in Developmental Time Series with Hidden-Markov Optimal Transport.- Unified integration of spatial transcriptomics across platforms.- Tree reconstruction guarantees from CRISPR-Cas9 lineage tracing data using Neighbor-Joining.- mcRigor: a statistical method to enhance the rigor of metacell partitioning in single-cell data analysis.- TissueMosaic enables cross-sample differential analysis of spatial transcriptomics datasets through self-supervised representation learning.- Accurate Detection of Tandem Repeats from Error-Prone Sequences with EquiRep.- ALPINE: an interpretable approach for decoding phenotypes from multi-condition sequencing data.- Synthetic control removes spurious discoveries from double dipping in single-cell and spatial transcriptomics data analyses.- Integer programming framework for pangenome-based genome inference.- A Partition Function Algorithm to Evaluate Inferred Subclonal Structures in Single-Cell Sequencing Data.- Untying Rates of Gene Gain and Loss Leads to a New Phylogenetic Approach.- Learning maximally spanning representations improves protein function annotation.- Optimal marker genes for c-separated cell types.- Bayesian Aggregation of Multiple Annotations Enhances Rare Variant Association Testing.- Steamboat: Attention-Based Multiscale Delineation of Cellular Interactions in Tissues.

Research in Computational Molecular Biology

Product form

£58.49

Includes FREE delivery

RRP £64.99 – you save £6.50 (10%)

Order before 4pm tomorrow for delivery by Tue 27 Jan 2026.

A Paperback by Sriram Sankararaman

1 in stock


    View other formats and editions of Research in Computational Molecular Biology by Sriram Sankararaman

    Publisher: Springer
    Publication Date: 5/23/2025
    ISBN13: 9783031902512, 978-3031902512
    ISBN10: 3031902513
    Also in:
    Computer science

    Description

    Book Synopsis

    Orientation-Aware Graph Neural Networks for Protein Structure Representation Learning.- Active Learning for Protein Structure Prediction.- Sequence-based TCR-Peptide Representations Using Cross-Epitope Contrastive Fine-tuning of Protein Language Models.- DualGOFiller: A Dual-Channel Graph Neural Network with Contrastive Learning for Enhancing Function Prediction in Partially Annotated Proteins.- Detecting antimicrobial resistance through MALDI-TOF mass spectrometry with statistical guarantees using conformal prediction.- Hierarchical Spatio-Temporal State-Space Modeling for fMRI Analysis.- A Phylogenetic Approach to Genomic Language Modeling.- Dynamic Programming Algorithms for Fast and Accurate Cell Lineage Tree Reconstruction from CRISPR-based Lineage Tracing Data.- Old dog, new tricks: Exact seeding strategy improves RNA design performances.- Scalable and Interpretable Identification of Minimal Undesignable RNA Structure Motifs with Rotational Invariance.- An Exact and Fast SAT Formulation for the DCJ Distance.- Improved pangenomic classification accuracy with chain statistics.- Dynamic μ-PBWT: Dynamic Run-length Compressed PBWT for Biobank Scale Data.- Prokrustean Graph: A substring index for rapid k-mer size analysis.- Rag2Mol: Structure-based drug design based on Retrieval Augmented Generation.- Rewiring protein sequence and structure generative models to enhance protein stability prediction.- Learning a CoNCISE language for small molecule binding and function.- An adversarial scheme for integrating multi-modal data on protein function.- Decoding the Functional Interactome of Non-Model Organisms with PHILHARMONIC.- The tree labeling polytope: a unified approach to ancestral reconstruction problems.- ScisTree2: An Improved Method for Large-scale Inference of Cell Lineage Trees and Genotype Calling from Noisy Single Cell Data.- OMKar: optical map based automated karyotyping of genomes to identify constitutional disorders.- TarDis: Achieving Robust and Structured Disentanglement of Multiple Covariates.- devider: long-read reconstruction of many diverse haplotypes.- Pharming: Joint Clonal Tree Reconstruction of SNV and CNA Evolution from Single-cell DNA Sequencing of Tumors.- GEM-Finder: dissecting GWAS variants via long-range interacting cis-regulatory elements with differentiation-specific genes.- Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states.- cfDecon: Accurate and interpretable methylation based cell type deconvolution for cell-free DNA.- Inferring cell differentiation maps from lineage tracing data.- Alignment-free estimation of read to genome distances and its applications.- ML-MAGES: A machine learning framework for multivariate genetic association analyses with genes and effect size shrinkage.- TX-Phase: Secure Phasing of Private Genomes in a Trusted Execution Environment.- Hyper-k-mers: efficient streaming k-mers representation.- Characterizing the Solution Space of Migration Histories of Metastatic Cancers with MACH2.- Causal Disentanglement of Treatment Effects in Single-cell RNA Sequencing through Counterfactual Inference.- Integration and querying of multimodal single-cell data with PoE-VAE.- ralphi: a deep reinforcement learning framework for haplotype assembly.- GeneCover: A Combinatorial Approach for Label-free Marker Gene Selection.- Joint imputation and deconvolution of gene expression across spatial transcriptomics platforms.- ScatTR: Estimating the Size of Long Tandem Repeat Expansions from Short-Reads.- Learning Latent Trajectories in Developmental Time Series with Hidden-Markov Optimal Transport.- Unified integration of spatial transcriptomics across platforms.- Tree reconstruction guarantees from CRISPR-Cas9 lineage tracing data using Neighbor-Joining.- mcRigor: a statistical method to enhance the rigor of metacell partitioning in single-cell data analysis.- TissueMosaic enables cross-sample differential analysis of spatial transcriptomics datasets through self-supervised representation learning.- Accurate Detection of Tandem Repeats from Error-Prone Sequences with EquiRep.- ALPINE: an interpretable approach for decoding phenotypes from multi-condition sequencing data.- Synthetic control removes spurious discoveries from double dipping in single-cell and spatial transcriptomics data analyses.- Integer programming framework for pangenome-based genome inference.- A Partition Function Algorithm to Evaluate Inferred Subclonal Structures in Single-Cell Sequencing Data.- Untying Rates of Gene Gain and Loss Leads to a New Phylogenetic Approach.- Learning maximally spanning representations improves protein function annotation.- Optimal marker genes for c-separated cell types.- Bayesian Aggregation of Multiple Annotations Enhances Rare Variant Association Testing.- Steamboat: Attention-Based Multiscale Delineation of Cellular Interactions in Tissues.

    Recently viewed products

    © 2026 Book Curl

      • American Express
      • Apple Pay
      • Diners Club
      • Discover
      • Google Pay
      • Maestro
      • Mastercard
      • PayPal
      • Shop Pay
      • Union Pay
      • Visa

      Login

      Forgot your password?

      Don't have an account yet?
      Create account