Handbook of statistical bioinformatics
著者
書誌事項
Handbook of statistical bioinformatics
(Springer handbooks of computational statistics)
Springer, c2022
2nd ed
大学図書館所蔵 全4件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references
Other editors: Bernhard Schölkopf, Martin T. Wells, Hongyu Zhao
内容説明・目次
内容説明
Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.
目次
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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