Computational and Statistical Approaches to Genomics
Wei Zhang · Ilya Shmulevich
mai 2007 · Springer Science & Business Media
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Computational and Statistical Genomics aims to help researchers deal with current genomic challenges. Topics covered include:
- overviews of the role of supercomputers in genomics research, the existing challenges and directions in image processing for microarray technology, and web-based tools for microarray data analysis;
- approaches to the global modeling and analysis of gene regulatory networks and transcriptional control, using methods, theories, and tools from signal processing, machine learning, information theory, and control theory;
- state-of-the-art tools in Boolean function theory, time-frequency analysis, pattern recognition, and unsupervised learning, applied to cancer classification, identification of biologically active sites, and visualization of gene expression data;
- crucial issues associated with statistical analysis of microarray data, statistics and stochastic analysis of gene expression levels in a single cell, statistically sound design of microarray studies and experiments; and
- biological and medical implications of genomics research.
Teave autori kohta
Wei Zhang is an Associate Professor in the Cancer Genomics Laboratory, University of Texas M.D. Anderson Cancer Center Ilya Shmulevich is an Assistant Professor in the Cancer Genomics Laboratory, University of Texas M.D. Anderson Cancer Center.
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