Computational and Statistical Approaches to Genomics

• 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.