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Computational and Statistical Approaches to Genomics

  • Book
  • © 2002

Overview

Editors:
  1. Wei Zhang

    1. University of Texas M. D. Anderson Cancer Center, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Ilya Shmulevich

    1. University of Texas M. D. Anderson Cancer Center, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-xiv
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  2. Microarray Image Analysis and Gene Expression Ratio Statistics

    • Yidong Chen, Edward R. Dougherty, Michael L. Bittner, Paul Meltzer, Jeffery Trent
    Pages 1-21

  3. Statistical Considerations in the Assessment of cDNA Microarray Data Obtained Using Amplification

    • Jing Wang, Kevin R. Coombes, Keith Baggerly, Limei Hu, Stanley R. Hamilton, Wei Zhang
    Pages 23-39

  4. Sources of Variation in Microarray Experiments

    • M. Kathleen Kerr, Edward H. Leiter, Laurent Picard, Gary A. Churchill
    Pages 41-51

  5. Studentizing Microarray Data

    • Keith A. Baggerly, Kevin R. Coombes, Kenneth R. Hess, David N. Stivers, Lynne V. Abruzzo, Wei Zhang
    Pages 53-64

  6. Exploratory Clustering of Gene Expression Profiles of Mutated Yeast Strains

    • Merja Oja, Janne Nikkilä, Petri Törönen, Garry Wong, Eero Castrén, Samuel Kaski
    Pages 65-78

  7. Selecting Informative Genes for Cancer Classification Using Gene Expression Data

    • Tatsuya Akutsu, Satoru Miyano
    Pages 79-91

  8. Design Issues and Comparison of Methods for Microarray-Based Classification

    • Edward R. Dougherty, Sanju N. Attoor
    Pages 93-111

  9. Analyzing Protein Sequences Using Signal Analysis Techniques

    • Karen M. Bloch, Gonzalo R. Arce
    Pages 113-124

  10. Statistics of the Numbers of Transcripts and Protein Sequences Encoded in the Genome

    • Vladimir A. Kuznetsov
    Pages 125-171

  11. Normalized Maximum Likelihood Models for Boolean Regression with Application to Prediction and Classification in Genomics

    • Ioan Tabus, Jorma Rissanen, Jaakko Astola
    Pages 173-189

  12. Inference of Genetic Regulatory Networks Via Best-Fit Extensions

    • Ilya Shmulevich, Antti Saarinen, Olli Yli-Harja, Jaakko Astola
    Pages 197-210

  13. Regularization and Noise Injection for Improving Genetic Network Models

    • Eugene van Someren, Lodewyk Wessels, Marcel Reinders, Eric Backer
    Pages 211-226

  14. Parallel Computation and Visualization Tools for Codetermination Analysis of Multivariate Gene Expression Relations

    • Edward B. Suh, Edward R. Dougherty, Seungchan Kim, Michael L. Bittner, Yidong Chen, Daniel E. Russ et al.
    Pages 227-240

  15. Human Glioma Diagnosis from Gene Expression Data

    • Gregory N. Fuller, Kenneth R. Hess, Cristian Mircean, Ioan Tabus, Ilya Shmulevich, Chang Hun Rhee et al.
    Pages 241-256

  16. Application of DNA Microarray Technology to Clinical Biopsies of Breast Cancer

    • Lajos Pusztai, W. Fraser Symmans, Thomas A. Buchholz, Jim Stec, Mark Ayers, Ed Clark et al.
    Pages 257-275

  17. Alternative Splicing: Genetic Complexity in Cancer

    • Sonya W. Song, Gilbert J. Cote, Chunlei Wu, Wei Zhang
    Pages 277-297

  18. Single-Nucleotide Polymorphisms, DNA Repair, and Cancer

    • Qingyi Wei, Erich M. Sturgis, Margaret R. Spitz, Harvey W. Mohrenweiser, Ilya Shmulevich, Shouming Kong et al.
    Pages 299-323

  19. Back Matter

    Pages 325-329
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Keywords

About this book

Computational and Statistical Genomics aims to help researchers deal with current genomic challenges. Topics covered include:

  • overviews of the role of supercomputers in genomics research, the existing challenges and directions in image processing for microarray technology, and web-based tools for microarray data analysis;
  • approaches to the global modeling and analysis of gene regulatory networks and transcriptional control, using methods, theories, and tools from signal processing, machine learning, information theory, and control theory;
  • state-of-the-art tools in Boolean function theory, time-frequency analysis, pattern recognition, and unsupervised learning, applied to cancer classification, identification of biologically active sites, and visualization of gene expression data;
  • crucial issues associated with statistical analysis of microarray data, statistics and stochastic analysis of gene expression levels in a single cell, statistically sound design of microarray studies and experiments; and
  • biological and medical implications of genomics research.

Editors and Affiliations

  • University of Texas M. D. Anderson Cancer Center, USA

    Wei Zhang, Ilya Shmulevich

About the editors

Wei Zhang is an Associate Professor in the Cancer Genomics Laboratory, University of Texas M.D. Anderson Cancer Center Ilya Shmulevich is an Assistant Professor in the Cancer Genomics Laboratory, University of Texas M.D. Anderson Cancer Center.

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