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