Genomic Signal Processing and Statistics

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Genomic Signal Processing and Statistics by Edward R. Dougherty (Repost)
Publisher: Hindawi Publishing Corporation | 2005-04 | ISBN: 9775945070 | 449 pages | PDF |
Recent advances in genomic studies have stimulated synergetic research and development in many cross-disciplinary areas. Genomic data, especially the recent large-scale microarray gene expression data, represents enormous challenges for signal processing and statistics in processing these vast data to reveal the complex biological functionality. 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 industry, in the fields of molecular biology, engineering, statistics, and signal processing.

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关键词：Processing Statistics statistic processI Process Statistics Processing Signal Genomic

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 differential 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 Stafford, Michael L. Bittner,
and Edward B. Suh ......................................... 409

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