Computational and Statistical Approaches to Genomics

  • Wei Zhang
  • Ilya Shmulevich

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Yidong Chen, Edward R. Dougherty, Michael L. Bittner, Paul Meltzer, Jeffery Trent
    Pages 1-21
  3. Jing Wang, Kevin R. Coombes, Keith Baggerly, Limei Hu, Stanley R. Hamilton, Wei Zhang
    Pages 23-39
  4. M. Kathleen Kerr, Edward H. Leiter, Laurent Picard, Gary A. Churchill
    Pages 41-51
  5. Keith A. Baggerly, Kevin R. Coombes, Kenneth R. Hess, David N. Stivers, Lynne V. Abruzzo, Wei Zhang
    Pages 53-64
  6. Merja Oja, Janne Nikkilä, Petri Törönen, Garry Wong, Eero Castrén, Samuel Kaski
    Pages 65-78
  7. Edward R. Dougherty, Sanju N. Attoor
    Pages 93-111
  8. Karen M. Bloch, Gonzalo R. Arce
    Pages 113-124
  9. Ilya Shmulevich, Antti Saarinen, Olli Yli-Harja, Jaakko Astola
    Pages 197-210
  10. Eugene van Someren, Lodewyk Wessels, Marcel Reinders, Eric Backer
    Pages 211-226
  11. Edward B. Suh, Edward R. Dougherty, Seungchan Kim, Michael L. Bittner, Yidong Chen, Daniel E. Russ et al.
    Pages 227-240
  12. Gregory N. Fuller, Kenneth R. Hess, Cristian Mircean, Ioan Tabus, Ilya Shmulevich, Chang Hun Rhee et al.
    Pages 241-256
  13. Lajos Pusztai, W. Fraser Symmans, Thomas A. Buchholz, Jim Stec, Mark Ayers, Ed Clark et al.
    Pages 257-275
  14. Sonya W. Song, Gilbert J. Cote, Chunlei Wu, Wei Zhang
    Pages 277-297
  15. Qingyi Wei, Erich M. Sturgis, Margaret R. Spitz, Harvey W. Mohrenweiser, Ilya Shmulevich, Shouming Kong et al.
    Pages 299-323
  16. Back Matter
    Pages 325-329

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.


Expression biopsy cell classification data analysis gene expression genomics image processing information theory microarray pattern recognition signal processing statistical analysis statistics transcription

Editors and affiliations

  • Wei Zhang
    • 1
  • Ilya Shmulevich
    • 1
  1. 1.University of Texas M. D. Anderson Cancer CenterUSA

Bibliographic information