Handbook of Statistical Bioinformatics

1. Front Matter

   Pages i-ix

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2. Sequence Analysis

   1. Front Matter

      Pages 1-1

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   2. Accuracy Assessment of Consensus Sequence from Shotgun Sequencing

      • Lei M. Li

      Pages 3-30

   3. Statistical and Computational Studies on Alternative Splicing

      • Liang Chen

      Pages 31-53

   4. Statistical Learning and Modeling of TF-DNA Binding

      • Bo Jiang, Jun S. Liu

      Pages 55-72

   5. Computational Promoter Prediction in a Vertebrate Genome

      • Michael Q. Zhang

      Pages 73-85

   6. Discovering Influential Variables: A General Computer Intensive Method for Common Genetic Disorders

      • Tian Zheng, Herman Chernoff, Inchi Hu, Iuliana Ionita-Laza, Shaw-Hwa Lo

      Pages 87-107

   7. STORMSeq: A Method for Ranking Regulatory Sequences by Integrating Experimental Datasets with Diverse Computational Predictions

      • Jim C. Huang, Brendan J. Frey

      Pages 109-134

   8. Mixture Tree Construction and Its Applications

      • Grace S. C. Chen, Mingze Li, Michael Rosenberg, Bruce Lindsay

      Pages 135-147

3. Expression Data Analysis

   1. Front Matter

      Pages 149-149

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   2. Experimental Designs and ANOVA for Microarray Data

      • Richard E. Kennedy, Xiangqin Cui

      Pages 151-169

   3. The MicroArray Quality Control (MAQC) Project and Cross-Platform Analysis of Microarray Data

      • Zhining Wen, Zhenqiang Su, Jie Liu, Baitang Ning, Lei Guo, Weida Tong et al.

      Pages 171-192

   4. A Survey of Classification Techniques for Microarray Data Analysis

      • Wai-Ki Yip, Samir B. Amin, Cheng Li

      Pages 193-223

   5. Statistical Analysis of Single Nucleotide Polymorphism Microarrays in Cancer Studies

      • Pierre Neuvial, Henrik Bengtsson, Terence P. Speed

      Pages 225-255

   6. Computational Analysis of ChIP-chip Data

      • Hongkai Ji

      Pages 257-282

   7. eQTL Mapping for Functional Classes of Saccharomyces cerevisiae Genes with Multivariate Sparse Partial Least Squares Regression

      • Dongjun Chung, Sündüz Keleş

      Pages 283-298

   8. Statistical Analysis of Time Course Microarray Data

      • Lingyan Ruan, Ming Yuan

      Pages 299-313

4. Systems Biology

   1. Front Matter

      Pages 315-315

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   2. Kernel Methods in Bioinformatics

      • Karsten M. Borgwardt

      Pages 317-334

   3. Graph Classification Methods in Chemoinformatics

      • Koji Tsuda

      Pages 335-351

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.

• bioinformatics
• computer science
• statistics

“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)

• , Institute of Statistics, National Chiao Tung University, Hsinchu, Taiwan R.O.C.

  Henry Horng-Shing Lu

• , Department of Empirical Inference, MPI for Intelligent Systems, Tübingen, Germany

  Bernhard Schölkopf

• School of Medicine, Dept. Epidemiology & Public Health, Yale University, New Haven, USA

  Hongyu Zhao

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.