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Computational and Statistical Approaches to Genomics / Edition 2

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Overview

The second edition of this book adds eight new contributors to reflect a modern cutting edge approach to genomics. It contains the newest research results on genomic analysis and modeling using state-of-the-art methods from engineering, statistics, and genomics. These tools and models are then applied to real biological and clinical problems. The book’s original seventeen chapters are also updated to provide new initiatives and directions.

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Editorial Reviews

From The Critics
Seventeen contributions cover a wide range of topics and span a number of disciplines such as image analysis, statistics, machine learning, pattern recognition, time-frequency and nonlinear signal processing, parallel computing, molecular biology, and others. Each chapter follows the various stages from data and analysis through synthesis and application. Topics include an overview of the role of supercomputers and other tools; approaches to the global modeling and analysis of gene regulatory networks and transcriptional control; state-of-the-art tools in Boolean function theory, time-frequency analysis, pattern recognition, and unsupervised learning; crucial issues associated with statistical analysis of microarray data, statistics and stochastic analysis of gene expression levels; and biological and medical implications of genomics research. Appropriate for use as an advanced level textbook, as well as for researchers. Edited by Zhang and Shmulevich (U. of Texas M.D. Anderson Cancer Center). Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780387262871
  • Publisher: Springer US
  • Publication date: 5/4/2006
  • Edition description: 2nd ed. 2006
  • Edition number: 2
  • Pages: 416
  • Product dimensions: 6.40 (w) x 9.30 (h) x 1.10 (d)

Table of Contents

Microarray Image Analysis and Gene Expression Ratio Statistics.- Statistical Considerations in the Assessment of cDNA Microarray Data Obtained Using Amplification.- Sources of Variation in Microarray Experiments.- Studentizing Microarray Data.- Exploratory Clustering of Gene Expression Profiles of Mutated Yeast Strains.- Selecting Informative Genes for Cancer Classification Using Gene Expression Data.- Finding Functional Structures in Glioma Gene-Expressions Using Gene Shaving Clustering and MDL Principle.- Design Issues and Comparison of Methods for Microarray-Based Classification.- Analyzing Protein Sequences Using Signal Analysis Techniques.- Statistical Methods in Serial Analysis of Gene Expression (SAGE).- Normalized Maximum Likelihood Models for Boolean Regression with Application to Prediction and Classification in Genomics.- Inference of Genetic Regulatory Networks.- Regularization and Noise Injection for Improving Genetic Network Models.- Parallel Computation and Visualization Tools for Codetermination Analysis of Multivariate Gene Expression Relations.- Single Nucleotide Polymorphisms and their Applications.- The Contribution of Alternative Transcription and Alternative Splicing to the Complexity of Mammalian Transcriptomes.- Computational Imaging, and Statistical Analysis of Tissue Microarrays.

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