Computational and Statistical Approaches to Genomics

✍ Scribed by Yidong Chen, Edward R. Dougherty (auth.), Wei Zhang, Ilya Shmulevich (eds.)

• Publisher: Springer US
• Year: 2006
• Tongue: English
• Leaves: 425
• Edition: 2nd ed.
• Category: Library

✦ Synopsis

Computational and Statistical Approaches to Genomics, 2^nd Edition, aims to help researchers deal with current genomic challenges. During the three years after the publication of the first edition of this book, the computational and statistical research in genomics have become increasingly more important and indispensable for understanding cellular behavior under a variety of environmental conditions and for tackling challenging clinical problems. In the first edition, the organizational structure was: data ? analysis ? synthesis ? application. In the second edition, the same structure remains, but the chapters that primarily focused on applications have been deleted.

This decision was motivated by several factors. Firstly, the main focus of this book is computational and statistical approaches in genomics research. Thus, the main emphasis is on methods rather than on applications. Secondly, many of the chapters already include numerous examples of applications of the discussed methods to current problems in biology.

The range of topics have been broadened to include newly contributed chapters on topics such as alternative splicing, tissue microarray image and data analysis, single nucleotide polymorphisms, serial analysis of gene expression, and gene shaving. Additionally, a number of chapters have been updated or revised.

This book is for any researcher, in academia and industry, in biology, computer science, statistics, or engineering involved in genomic problems. It can also be used as an advanced level textbook in a course focusing on genomic signals, information processing, or genome biology.

✦ Table of Contents

Content:
Front Matter....Pages i-ix
Microarray Image Analysis and Gene Expression Ratio Statistics....Pages 1-19
Statistical Considerations in the Assessment of cDNA Microarray Data Obtained Using Amplification....Pages 21-36
Sources of Variation in Microarray Experiments....Pages 37-47
Studentizing Microarray Data....Pages 49-59
Exploratory Clustering of Gene Expression Profiles of Mutated Yeast Strains....Pages 61-74
Selecting Informative Genes for Cancer Classification Using Gene Expression Data....Pages 75-88
Finding Functional Structures in Ggioma Gene-Expressions Using Gene Shaving Clustering and MDL Principle....Pages 89-118
Design Issues and Comparison of Methods for Microarray-Based Classification....Pages 119-136
Analyzing Protein Sequences Using Signal Analysis Techniques....Pages 137-161
Scale-Dependent Statistics of the Numbers of Transcripts and Protein Sequences Encoded in the Genome....Pages 163-208
Statistical Methods in Serial Analysis of Gene Expression (Sage)....Pages 209-233
Normalized Maximum Likelihood Models for Boolean Regression with Application to Prediction and Classification in Genomics....Pages 235-258
Inference of Genetic Regulatory Networks via Best-Fit Extensions....Pages 259-278
Regularization and Noise Injection for Improving Genetic Network Models....Pages 279-295
Parallel Computation and Visualization Tools for Codetermination Analysis of Multivariate Gene Expression Relations....Pages 297-310
Single Nucleotide Polymorphisms and Their Applications....Pages 311-349
The Contribution of Alternative Transcription and Alternative Splicing to the Complexity of Mammalian Transcriptomes....Pages 351-380
Computational Imaging, and Statistical Analysis of Tissue Microarrays: Quantitative Automated Analysis of Tissue Microarrays....Pages 381-403
Back Matter....Pages 405-416