Computational and Statistical Approaches to Genomics

At the beginning of the post-sequencing era, biology must 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. This text aims to help researchers deal with contemporary 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.

Table of Contents:
Microarray Image Analysis and Gene Expression Ratio Statistics, Y. Chen, et al; Statistical Considerations in the Assessment of cDNA Microarray Data Obtained Using Amplification, J. Wang, et al; Sources of Variation in Microarray Experiments, M.K. Kerr, et al; Studentizing Microarray Data, K.A. Baggerly, et al; Exploratory Clustering of Gene Expression Profiles of Mutated Yeast Strains, M. Oja, et al; Selecting Informative Genes for Cancer Classification Using Gene Expression Data, T. Akutsu, S. Miyano; Design Issues and Commparison of Methods for Microarray-Based Classification, E.R. Dougherty, S.N. Attoor; Analyzing Protein Sequences using Signal Analysis Techniques, K.M. Bloch, G.R. Arce; Statistics of the Numbers of Transcripts and Protein Sequences Encoded in the Genome, V.A. Kuznetsov; Normalized Maximum Likelihood Models for Boolean Regression Used for Prediction and Classification in Genomics, I. Tabus, et al; Inference of Genetic Regulatory Networks via Best-Fit Extensions, I. Shmulevich, et al; Regularization and Noise Injection for Improving Genetic Network Models, E. van Someren, et al; Parallel Computation and Visualization Tools for Codetermination Analysis of Multivariate Gene-Expression Relations, E.B. Suh, et al; Human Glioma Diagnosis from Gene Expression Data, G.N. Fuller, et al; Application of DNA Microarray Technology to Clinical Biopsies of Breast Cancer, L. Pusztai, et al; Alternative Splicing - Genetic Complexity in Cancer, S.W. Song, et al; Single-Nucleotide Polymorphisms, DNA Repair, and Cancer, Q. Wei, et al.