About the Book
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 micro array 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 micro array 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.
Table of Contents:
Genomic signal processing: perspectives, Edward R. Dougherty, Ilya Shmulevich, Jie Chen, and Z. Jane Wang 1 Part I. Sequence Analysis 1. Representation and analysis of DNA sequences, Paul Dan Cristea 15 Part II. Signal Processing and StatisticsMethodologies in Gene Selection 2. Gene feature selection, Ioan Tabus and Jaakko Astola 67 3. Classification, Ulisses Braga-Neto and Edward R. Dougherty 93 4. Clustering: revealing intrinsic dependencies in microarray data, Marcel Brun, Charles D. Johnson, and Kenneth S. Ramos 129 5. From biochips to laboratory-on-a-chip system, Lei Wang, Hongying Yin, and Jing Cheng 163 Part III. Modeling and Statistical Inference of Genetic Regulatory Networks 6. Modeling and simulation of genetic regulatory networks by ordinary di.erential equations, Hidde de Jong and Johannes Geiselmann 201 7. Modeling genetic regulatory networks with probabilistic Boolean networks, Ilya Shmulevich and Edward R. Dougherty 241 8. Bayesian networks for genomic analysis, Paola Sebastiani, Maria M. Abad, and Marco F. Ramoni 281 9. Statistical inference of transcriptional regulatory networks, Xiaodong Wang, Dimitris Anastassiou, and Dong Guo 321 Part IV. Array Imaging, Signal Processing in Systems Biology, and Applications in Disease Diagnosis and Treatments 10. Compressing genomic and proteomic array images for statistical analyses, Rebecka J ornsten and Bin Yu 341 11. Cancer genomics, proteomics, and clinic applications, X. Steve Fu, Chien-an A. Hu, Jie Chen, Z. Jane Wang, and K. J. Ray Liu 367 12. Integrated approach for computational systems biology, Seungchan Kim, Phillip Sta.ord, Michael L. Bittner, and Edward B. Suh 409
About the Author :
Edward R. Dougherty is a Professor in the Department of Electrical Engineering at Texas A&M University in College Station. He holds a Ph.D. in mathematics from Rutgers University and an M.S. in Computer Science from Stevens Institute of Technology. He is author of 11 books and over 100 journal papers. He has also edited four collections. He is an SPIE fellow and served as editor of the Journal of Electronic Imaging for six years. Ilya Shmulevich is an Assistant Professor at the Cancer Genomics Laboratory, the University of Texas M. D. Anderson Cancer Center and an adjunct professor at the Department of Statistics, Rice University; he been recently appointed by Institute for Systems Biology in Seattle. Research interest: systems biology; computational biology; genomics; signal and image processing; machine learning. His research focuses on computational and mathematical modeling of genetic regulatory networks, their inference from genomic and proteomic measurement data, prediction of molecular pathways, development of algorithms for cancer classification, and design of novel high-throughput genomic technologies. Current research is directed at understanding how a cell ehavior is governed by a complex dynamical system of genetic interactions and how these systems fail in disease, such as cancer. An important aspect of this research is the study of differentiation and cellular homeostatic stability ! from a systems perspective. Jie Chen is an Assistant Professor in the Division of Engineering at Brown University, Providence, Road Island in USA. He received his Ph.D. and M.S. degree in Electrical Engineering from University of Maryland, College Park. He obtained his undergraduate B.S. degree from Fudan University, Shanghai, China. Since June 2002, he has been an Assistant Professor at University in the Division of Engineering. From 2000 to 2002, he helped to found two start-up companies (Ibiquity and flarion) in the areas of digital terrestrial radio and four-generation wireless networking. He is alos a distinguished lecturer of the IEEE circuit and system society, and a senior member of the IEEE signal processing society. His current nanotechnology research is supported by two NSF Nanoscale Exploratory Research grants. He is an associate editor for IEEE Signal Processing Magazine, and has been associated editors for IEEE Trans. on Multimedia and EURASIP Journal on Applied Signal Processing. Dr. Ch! en serves as a technical program co-chair of IEEE workshop on Genomic Signal Processing and Statistics He is the secretary of life science system and application technical committee of the IEEE circuit and system society. He authored or co-authored about 40 transaction and conference proceeding papers: co-editing the book omic Signal Processing and Statistics URASIP book series 2004), and co-authored the book ''Design of Digital Video Coding Systems: A Complete Compressed Domain Approach'' (New York: Marcel Dekker 2001). Z. JANE WANG is an Assistant Professor in the Electrical and Computer Engineering Department, University of British Columbia, Canada. Dr. Wang received the B.Sc. degree from Tsinghua University, China, in 1996 (with the highest honor), and the M.Sc. and Ph.D. degrees from the University of Connecticut in 2000 and 2002 (with the Outstanding Engineering Doctoral Student Award), respectively, all in electrical engineering. From 2002 to 2004, She was a Research Associate in Electrical and Computer Engineering Department and Institute for Systems Research at the University of Maryland. Since Aug. 2004, she has been an Assistant Professor in the Department of Electrical and Computer Engineering at University of British Columbia. Dr. Wang's research interests are in the broad areas of statistical signal processing, information security, wireless communications and genomic signal processing and statistics.
Review :
"This work provides a tutorial-level overview of genomic signal processing (GSP) and statistics. The book is accessible to researchers in academia and industry who are interested in cross-disciplinary areas relating to molecular biology, engineering, statistics, and signal processing. After a history of genomic research and a background introduction from both biological and signal- processing/statistical perspectives, sections focus on signal processing and statistics methods in gene selection, signal processing in genomic network modeling and analysis, and microarray imaging, signal processing in systems biology, and applications in disease diagnosis and treatments" SciTech Book News September 2005, Vol. 29, No. 3
