Computational and Statistical Approaches to Genomics

Author:   Wei Zhang ,  Ilya Shmulevich
Publisher:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of hardcover 2nd ed. 2006
ISBN:  

9781441938824


Pages:   416
Publication Date:   19 November 2010
Format:   Paperback
Availability:   Out of stock   Availability explained
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Computational and Statistical Approaches to Genomics


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Overview

The 2nd edition of this book adds 8 new contributors to reflect a modern cutting edge approach to genomics. The expanded scope includes coverage of statistical issues on single nucleotide polymorphism analysis array, CGH analysis, SAGE analysis, gene shaving and related methods for microarray data analysis, and cross-hybridization issues on oligo arrays. The authors of the 17 original chapters have updated the contents of their chapters, including references, on such topics as the development of novel engineering, statistical and computational principles, as well as methods, models, and tools from these disciplines applied to genomics.

Full Product Details

Author:   Wei Zhang ,  Ilya Shmulevich
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of hardcover 2nd ed. 2006
Dimensions:   Width: 15.50cm , Height: 2.20cm , Length: 23.50cm
Weight:   0.670kg
ISBN:  

9781441938824


ISBN 10:   1441938826
Pages:   416
Publication Date:   19 November 2010
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Out of stock   Availability explained
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

Table of Contents

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.- Finding Functional Structures in Ggioma Gene-Expressions Using Gene Shaving Clustering and MDL Principle.- Design Issues and Comparison of Methods for Microarray-Based Classification.- Analyzing Protein Sequences Using Signal Analysis Techniques.- Scale-Dependent Statistics of the Numbers of Transcripts and Protein Sequences Encoded in the Genome.- Statistical Methods in Serial Analysis of Gene Expression (Sage).- 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.- Single Nucleotide Polymorphisms and Their Applications.- The Contribution of Alternative Transcription and Alternative Splicing to the Complexity of Mammalian Transcriptomes.- Computational Imaging, and Statistical Analysis of Tissue Microarrays: Quantitative Automated Analysis of Tissue Microarrays.

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