Handbook of Statistical Bioinformatics (Springer Handbooks of Computational Statistics) （2011）

Lu, Henry Horng-Shing (EDT)/ Schölkopf, Bernhard (EDT)/ Zhao, Hongyu (EDT)

Springer-Verlag Berlin and Heidelberg GmbH & Co. K（2013/07発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 630 p.
• 言語 ENG
• 商品コード 9783642268274
• DDC分類 572.80285

Full Description

Numerous fascinating breakthroughs in biotechnology have generated large volumes and diverse types of high throughput data that demand the development of efficient and appropriate tools in computational statistics integrated with biological knowledge and computational algorithms. This volume collects contributed chapters from leading researchers to survey the many active research topics and promote the visibility of this research area. This volume is intended to provide an introductory and reference book for students and researchers who are interested in the recent developments of computational statistics in computational biology.

Contents

I: Accuracy Assessment of Consensus Sequence from Shotgun Sequencing.- Statistical and Computational Studies on Alternative Splicing.- Using Sequence Information to Predict TF-DNA Binding.- Computational Promoter Prediction in a Vertebrate Genome.- Discovering Influential Variables: A General Computer Intensive Method for Common Genetic Disorders.- STORMSeq: A Method for Ranking Regulatory Sequences by Integrating Experimental Datasets with Diverse Computational Predictions.- Mixture Tree Construction and Its Applications.- II: Experimental Designs and ANOVA for Microarray Data.- MAQC and Cross Platform Analysis of Microarray Data.- A Survey of Classification Techniques for Microarray Analysis.- Statistical Analysis of Single Nucleotide Polymorphism Microarrays in Cancer Studies.- Computational Analysis of ChIP-chip Data.- eQTL Mapping for Functional Classes of Saccharomyces Cerevisiae Genes with Multivariate Sparse Partial Least Squares Regression.- Analysis of Time Course Data.- III: Kernel Methods in Bioinformatics.- Graph Classification Methods in Chemoinformatics.- Hidden Markov Random Field Models for Network-based Analysis of Genomic Data.- Review of Weighted Gene Coexpression Network Analysis.- Liquid Association.- Boolean Networks.- Protein Interaction Networks: Protein Domain Interaction and Protein Function Prediction.- Regulatory Networks.- Inferring Signaling and Gene Regulatory Network from Genetic and Genomic Information.- Computational Drug Target Pathway Discovery: A Bayesian Network Approach.- Cancer Systems Biology.- Comparative Genomics and Molecular Evolution.- Robust Control of Immune Systems under Noises: Stochastic Game Approach.