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.
About: 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.
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