This volume covers key topics in modeling and analysis of massive data sets generated from high throughput biotechnology. It provides an introductory and reference book for students and researchers.

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.

Introduces the state-of-arts techniques for statistical bioinformatics Focus on the interface between computational statistics and computational biology Covers key topics in modeling and analysis of massive data sets generated from high throughput biotechnology Includes supplementary material: sn.pub/extras

Autorentext
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.

Inhalt

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.