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{ "item_title" : "Handbook of Statistical Bioinformatics", "item_author" : [" Henry Horng-Shing Lu", "Bernhard Schölkopf", "Martin T. Wells "], "item_description" : "Preface.- Part I Single-cell Analysis.- Computational and statistical methods for single-cell RNA sequencing data.- Pre-processing, dimension reduction, and clustering for single-cell RNA-seq data.- Integrative analyses of single-cell multi-omics data: a review from a statistical perspective.- Approaches to marker gene identification from single-cell RNA-sequencing data.- Model-based clustering of single-cell omics data.- Deep learning methods for single cell omics data.- Part II Network Analysis.- Probabilistic Graphical Models for Gene Regulatory Networks.- Additive conditional independence for large and complex biological structures.- Integration of Boolean and Bayesian Networks.- Computational methods for identifying microRNA-gene regulatory modules.- Causal inference in biostatistics.- Bayesian Balance Mediation Analysis in Microbiome Studies.- Part III Systems Biology.- Identifying genetic loci associated with complex trait variability.- Cell Type Specific Analysis for Gene Expression and DNA Methylation.- Recent development of computational methods in the field of epitranscriptomics.- Estimation of Tumor Immune Signatures from Transcriptomics Data.- Cross-Linking Mass Spectrometry Data Analysis.- Cis-regulatory Element Frequency Modules and their Phase Transition across Hominidae.- Improving tip-dating and rooting a viral phylogeny by modeling evolutionary rate as a function of time.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/3/66/265/901/3662659018_b.jpg", "price_data" : { "retail_price" : "219.99", "online_price" : "219.99", "our_price" : "219.99", "club_price" : "219.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Handbook of Statistical Bioinformatics|Henry Horng-Shing Lu

Handbook of Statistical Bioinformatics

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Overview

Preface.- Part I Single-cell Analysis.- Computational and statistical methods for single-cell RNA sequencing data.- Pre-processing, dimension reduction, and clustering for single-cell RNA-seq data.- Integrative analyses of single-cell multi-omics data: a review from a statistical perspective.- Approaches to marker gene identification from single-cell RNA-sequencing data.- Model-based clustering of single-cell omics data.- Deep learning methods for single cell omics data.- Part II Network Analysis.- Probabilistic Graphical Models for Gene Regulatory Networks.- Additive conditional independence for large and complex biological structures.- Integration of Boolean and Bayesian Networks.- Computational methods for identifying microRNA-gene regulatory modules.- Causal inference in biostatistics.- Bayesian Balance Mediation Analysis in Microbiome Studies.- Part III Systems Biology.- Identifying genetic loci associated with complex trait variability.- Cell Type Specific Analysis for Gene Expression and DNA Methylation.- Recent development of computational methods in the field of epitranscriptomics.- Estimation of Tumor Immune Signatures from Transcriptomics Data.- Cross-Linking Mass Spectrometry Data Analysis.- Cis-regulatory Element Frequency Modules and their Phase Transition across Hominidae.- Improving tip-dating and rooting a viral phylogeny by modeling evolutionary rate as a function of time.

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Details

  • ISBN-13: 9783662659014
  • ISBN-10: 3662659018
  • Publisher: Springer
  • Publish Date: December 2022
  • Dimensions: 9.21 x 6.14 x 0.94 inches
  • Shipping Weight: 1.68 pounds
  • Page Count: 410

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