Handbook of Statistical Bioinformatics

Henry Horng-Shing Lu is a Professor at the National Chiao Tung University's Institute of Statistics in Taiwan. He is also the Chairman of the University's Interdisciplinary Sciences Degree Program in the College of Science. His research interests are in the field of interdisciplinary studies related to statistics, medical images and bioinformatics.Bernhard Schölkopf is a member of the Max Planck Society and Director of the Max Planck Institute for Biological Cybernetics. He is also an Honorary Professor of Machine Learning at the Technical University Berlin. His scientific interests are in the field of inference from empirical data; in particular, in machine learning methods for extracting statistical and causal regularities.Hongyu Zhao is a Professor of Biostatistics, Statistics, and Genetics at Yale University, where he also serves as the Director of the Center for Statistical Genomics and Protoemics. His research interests include statistical genomics, computational biology, statistical proteomics, risk prediction, high dimensional data analysis, and network modeling and inference.

• 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.

“This book puts together a nice collection of statistical methods covering a wide range of research topics in computational biology. … I can recommend the book as an overview on methods applied in computational biology for readers already experienced in basic computational statistics. Especially readers interested in systems biology topics will find a comprehensive summary of methods.” (Marc Zapatka, Biometrical Journal, Vol. 55 (4), 2013)