Skip to main content
SpringerLink
Log in
Book cover

Handbook of Statistical Bioinformatics

  • Book
  • © 2011

Overview

Editors:
  1. Henry Horng-Shing Lu

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

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Bernhard Schölkopf

    1. , Department of Empirical Inference, MPI for Intelligent Systems, Tübingen, Germany

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Hongyu Zhao

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

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • 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

Part of the book series: Springer Handbooks of Computational Statistics (SHCS)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (27 chapters)

  1. Front Matter

    Pages i-ix
    Download chapter PDF

  2. Sequence Analysis

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    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
      Download chapter PDF

    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
      Download chapter PDF

    2. Kernel Methods in Bioinformatics

      • Karsten M. Borgwardt
      Pages 317-334

    3. Graph Classification Methods in Chemoinformatics

      • Koji Tsuda
      Pages 335-351
Back to top

Keywords

About this book

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.

Reviews

“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)

Editors and Affiliations

  • , 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

About the editors

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.

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation