Genomic Signal Processing and Statistics
Edited by Edward R. Dougherty, Ilya Shmulevich, Jie Chen, and Z. Jane Wang Recent advances in genomic studies have stimulated synergetic
research and development in many cross-disciplinary areas.
Processing the vast genomic data, especially the recent
large-scale microarray gene expression data, to reveal the complex
biological functionality, represents enormous challenges to signal
processing and statistics. This perspective naturally leads to a
new field, genomic signal processing (GSP), which studies the
processing of genomic signals by integrating the theory of signal
processing and statistics. Written by an international,
interdisciplinary team of authors, this invaluable edited volume
is accessible to students just entering this emergent field, and
to researchers, both in academia and in industry, in the fields of
molecular biology, engineering, statistics, and signal processing.
The book provides tutorial-level overviews and addresses the
specific needs of genomic signal processing students and
researchers as a reference book.
The book aims to address current genomic challenges by exploiting
potential synergies between genomics, signal processing, and
statistics, with special emphasis on signal processing and
statistical tools for structural and functional understanding of
genomic data. The first part of this book provides a brief history
of genomic research and a background introduction from both
biological and signal-processing/statistical perspectives, so that
readers can easily follow the material presented in the rest of
the book. In what follows, overviews of state-of-the-art
techniques are provided. We start with a chapter on sequence
analysis, and follow with chapters on feature selection,
classification, and clustering of microarray data. We then discuss
the modeling, analysis, and simulation of biological regulatory
networks, especially gene regulatory networks based on Boolean and
Bayesian approaches. Visualization and compression of gene data,
and supercomputer implementation of genomic signal processing
systems are also treated. Finally, we discuss systems biology and
medical applications of genomic research as well as the future
trends in genomic signal processing and statistics research.