Genomic Signal Processing

Description

Genomic signal processing (GSP) can be defined as the analysis, processing, and use of genomic signals to gain biological knowledge, and the translation of that knowledge into systems-based applications that can be used to diagnose and treat genetic diseases. Situated at the crossroads of engineering, biology, mathematics, statistics, and computer science, GSP requires the development of both nonlinear dynamical models that adequately represent genomic regulation, and diagnostic and therapeutic tools based on these models. This book facilitates these developments by providing rigorous mathematical definitions and propositions for the main elements of GSP and by paying attention to the validity of models relative to the data. Ilya Shmulevich and Edward Dougherty cover real-world situations and explain their mathematical modeling in relation to systems biology and systems medicine. Genomic Signal Processing makes a major contribution to computational biology, systems biology, and translational genomics by providing a self-contained explanation of the fundamental mathematical issues facing researchers in four areas: classification, clustering, network modeling, and network intervention.

Ilya Shmulevich, an associate professor at the Institute for Systems Biology, is the coauthor of "Microarray Quality Control" and the coeditor of "Computational and Statistical Approaches to Genomics". Edward R. Dougherty is professor of electrical and computer engineering and director of the Genomic Signal Processing Laboratory at Texas A&M University, and director of the Computational Biology Division at the Translational Genomics Research Institute. His thirteen previous books include "Random Processes for Image and Signal Processing".

"Genomic Signal Processing makes a major contribution to computational biology, systems biology, and translational genomics by providing a self-contained explanation of the fundamental mathematical issues facing researchers in four areas: classification, clustering, network modeling, and network intervention... The authors' substantial accomplishments in this area will inspire researchers and students alike. The book provides a much-needed stepping stone so that researchers can cross the gap on this front in their efforts. Also assuredly, it will be a delight to read for anyone who is encountering the topic for the first time and is wishing to exploit the current findings and interpretations in systems biology."--Current Engineering Practice "Overall, this book should be useful for individuals with a background in computer science and machine learning who wish to see the applications of mathematics to genomics."--Leon Glass, SIAM Review