Computational and Statistical Approaches to Genomics

by Zhang, Wei; Shmulevich, Ilya

• ISBN13: 9781402070235
• ISBN10: 1402070233
• Edition: 1st
• Format: Hardcover
• Copyright: 2002-03-01
• Publisher: Kluwer Academic Pub

Summary

At the beginning of the post-sequencing era, biology must now work with the enormous amounts of quantitative data being amassed and must render complex problems in mathematical terms, with all of the computational effort that entails. This phenomenon is perhaps best exemplified by the interdisciplinary scientific activity caused by the advent of high-throughput cDNA microarray technology, which facilitates large-scale surveys of gene expression. Biologists must now work together with engineers, statisticians, computer scientists, and other specialists, in order to attain a holistic understanding of the complex relationship between genes within the genome and uncover genetic function and regulation. 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. This book is for any researcher, in academia and industry, in biology, computer science, statistics, or engineering, involved in genomic problems. It could also be used as an advanced level textbook in a course focusing on genomic signals, information processing, or genome biology.

Table of Contents

Foreword	ix
Preface	xi
Microarray Image Analysis and Gene Expression Ratio Statistics	1	(22)
Yidong Chen
Edward R. Dougherty
Michael L. Bittner
Paul Meltzer
Jeffery Trent
Statistical Considerations in the Assessment of cDNA Microarray Data Obtained Using Amplification	23	(18)
Jing Wang
Kevin R. Coombes
Keith Baggerly
Limei Hu
Stanley R. Hamilton
Wei Zhang
Sources of Variation in Microarray Experiments	41	(12)
M. Kathleen Kerr
Edward H. Leiter
Laurent Picard
Gary A. Churchill
Studentizing Microarray Data	53	(12)
Keith A. Baggerly
Kevin R. Coombes
Kenneth R. Hess
David N. Stivers
Lynne V. Abruzzo
Wei Zhang
Exploratory Clustering of Gene Expression Profiles of Mutated Yeast Strains	65	(14)
Merja Oja
Janne Nikkila
Petri Toronen
Garry Wong
Eero Castren
Samuel Kaski
Selecting Informative Genes for Cancer Classification Using Gene Expression Data	79	(14)
Tatsuya Akutsu
Satoru Miyano
Design Issues and Comparison of Methods for Microarray-Based Classification	93	(20)
Edward R. Dougherty
Sanju N. Attoor
Analyzing Protein Sequences using Signal Analysis Techniques	113	(12)
Karen M. Bloch
Gonzalo R. Arce
Statistics of the Numbers of Transcripts and Protein Sequences Encoded in the Genome	125	(48)
Vladimir A. Kuznetsov
Normalized Maximum Likelihood Models for Boolean Regression Used for Prediction and Classification in Genomics	173	(24)
Ioan Tabus
Jorma Rissanen
Jaakko Astola
Inference of Genetic Regulatory Networks via Best-Fit Extensions	197	(14)
Ilya Shmulevich
Antti Saarinen
Olli Yli-Harja
Jaakko Astola
Regularization and Noise Injection for Improving Genetic Network Models	211	(16)
Eugene van Someren
Lodewyk Wessels
Marcel Reinders
Eric Backer
Parallel Computation and Visualization Tools for Codetermination Analysis of Multivariate Gene-Expression Relations	227	(14)
Edward B. Suh
Edward R. Dougherty
Seungchan Kim
Michael L. Bittner
Yidong Chen
Daniel E. Russ
Robert L. Martino
Human Glimo Diagnosis from Gene Expression Data	241	(16)
Gregory N. Fuller
Kenneth R. Hess
Cristian Mircean
Ioan Tabus
Ilya Shmulevich
Chang Hun Rhee
Kenneth D. Aldape
Janet M. Bruner
Raymond A. Sawaya
Wei Zhang
Application of DNA Microarray Technology to Clinical Biopsies of Breast Cancer	257	(20)
Lajos Pusztai
W. Fraser Symmans
Thomas A. Buchholz
Jim Stec
Mark Ayers
Ed Clark
Funda Meric
David Stivers
Kenneth Hess
Alternative Splicing: Genetic Complexity in Cancer	277	(22)
Sonya W. Song
Gilbert J. Cote
Chunlei Wu
Wei Zhang
Single-Nucleotide Polymorphisms, DNA Repair, and Cancer	299
Qingyi Wei
Erich M. Sturgis
Margaret R. Spitz
Harvey W. Mohrenweiser
Ilya Shmulevich
Shouming Kong
David Cogdell
Qing Mi
Wei Zhang