Ebook: Computational and Statistical Approaches to Genomics

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.

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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.

Content:
Front Matter....Pages i-xiv
Microarray Image Analysis and Gene Expression Ratio Statistics....Pages 1-21
Statistical Considerations in the Assessment of cDNA Microarray Data Obtained Using Amplification....Pages 23-39
Sources of Variation in Microarray Experiments....Pages 41-51
Studentizing Microarray Data....Pages 53-64
Exploratory Clustering of Gene Expression Profiles of Mutated Yeast Strains....Pages 65-78
Selecting Informative Genes for Cancer Classification Using Gene Expression Data....Pages 79-91
Design Issues and Comparison of Methods for Microarray-Based Classification....Pages 93-111
Analyzing Protein Sequences Using Signal Analysis Techniques....Pages 113-124
Statistics of the Numbers of Transcripts and Protein Sequences Encoded in the Genome....Pages 125-171
Normalized Maximum Likelihood Models for Boolean Regression with Application to Prediction and Classification in Genomics....Pages 173-189
Inference of Genetic Regulatory Networks Via Best-Fit Extensions....Pages 197-210
Regularization and Noise Injection for Improving Genetic Network Models....Pages 211-226
Parallel Computation and Visualization Tools for Codetermination Analysis of Multivariate Gene Expression Relations....Pages 227-240
Human Glioma Diagnosis from Gene Expression Data....Pages 241-256
Application of DNA Microarray Technology to Clinical Biopsies of Breast Cancer....Pages 257-275
Alternative Splicing: Genetic Complexity in Cancer....Pages 277-297
Single-Nucleotide Polymorphisms, DNA Repair, and Cancer....Pages 299-323
Back Matter....Pages 325-329

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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.

Content:
Front Matter....Pages i-xiv
Microarray Image Analysis and Gene Expression Ratio Statistics....Pages 1-21
Statistical Considerations in the Assessment of cDNA Microarray Data Obtained Using Amplification....Pages 23-39
Sources of Variation in Microarray Experiments....Pages 41-51
Studentizing Microarray Data....Pages 53-64
Exploratory Clustering of Gene Expression Profiles of Mutated Yeast Strains....Pages 65-78
Selecting Informative Genes for Cancer Classification Using Gene Expression Data....Pages 79-91
Design Issues and Comparison of Methods for Microarray-Based Classification....Pages 93-111
Analyzing Protein Sequences Using Signal Analysis Techniques....Pages 113-124
Statistics of the Numbers of Transcripts and Protein Sequences Encoded in the Genome....Pages 125-171
Normalized Maximum Likelihood Models for Boolean Regression with Application to Prediction and Classification in Genomics....Pages 173-189
Inference of Genetic Regulatory Networks Via Best-Fit Extensions....Pages 197-210
Regularization and Noise Injection for Improving Genetic Network Models....Pages 211-226
Parallel Computation and Visualization Tools for Codetermination Analysis of Multivariate Gene Expression Relations....Pages 227-240
Human Glioma Diagnosis from Gene Expression Data....Pages 241-256
Application of DNA Microarray Technology to Clinical Biopsies of Breast Cancer....Pages 257-275
Alternative Splicing: Genetic Complexity in Cancer....Pages 277-297
Single-Nucleotide Polymorphisms, DNA Repair, and Cancer....Pages 299-323
Back Matter....Pages 325-329
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