Computational and Statistical Approaches to Genomics
1st Edition
Published on 8. May 2007
XIV, 329 pages
978-0-306-47825-3 (ISBN)
Description
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.
More details
Other editions
New editions
Wei Zhang | Ilya Shmulevich
Computational and Statistical Approaches to Genomics
E-Book
12/2007
2nd Edition
Springer
€149.79
Available for download
Additional editions
Wei Zhang | Ilya Shmulevich | Zhang Wei
Computational and Statistical Approaches to Genomics
Book
03/2002
Kluwer Academic Publishers
€131.48
Article exhausted; check for reprint
Persons
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.
Content
Microarray Image Analysis and Gene Expression Ratio Statistics.- Statistical Considerations in the Assessment of cDNA Microarray Data Obtained Using Amplification.- Sources of Variation in Microarray Experiments.- Studentizing Microarray Data.- Exploratory Clustering of Gene Expression Profiles of Mutated Yeast Strains.- Selecting Informative Genes for Cancer Classification Using Gene Expression Data.- Design Issues and Comparison of Methods for Microarray-Based Classification.- Analyzing Protein Sequences Using Signal Analysis Techniques.- Statistics of the Numbers of Transcripts and Protein Sequences Encoded in the Genome.- Normalized Maximum Likelihood Models for Boolean Regression with Application to Prediction and Classification in Genomics.- Inference of Genetic Regulatory Networks Via Best-Fit Extensions.- Regularization and Noise Injection for Improving Genetic Network Models.- Parallel Computation and Visualization Tools for Codetermination Analysis of Multivariate Gene Expression Relations.- Human Glioma Diagnosis from Gene Expression Data.- Application of DNA Microarray Technology to Clinical Biopsies of Breast Cancer.- Alternative Splicing: Genetic Complexity in Cancer.- Single-Nucleotide Polymorphisms, DNA Repair, and Cancer.
