Computational and Statistical Approaches to Genomics (eBook, PDF)

Redaktion: Zhang, Wei; Shmulevich, Ilya

• Format: PDF

Produktbeschreibung

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 à analysis à synthesis à 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.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 416
• Erscheinungstermin: 26. Dezember 2007
• Englisch
• ISBN-13: 9780387262888
• Artikelnr.: 37287354

Autorenporträt

Ilya Shmulevich received his Ph.D. degree in Electrical and Computer Engineering from Purdue University, West Lafayette, Indiana in 1997. In 1997-1998, he was a postdoctoral researcher at the Nijmegen Institute for Cognition and Information at the University of Nijmegen and National Research Institute for Mathematics and Computer Science at the University of Amsterdam in The Netherlands, where he studied computational models of music perception and recognition. In 1998-2000, he worked as a senior researcher at the Tampere International Center for Signal Processing at the Signal Processing Laboratory in Tampere University of Technology, Tampere, Finland. Presently, he is an Assistant Professor at the Cancer Genomics Laboratory at The University of Texas M. D. Anderson Cancer Center in Houston, TX.

Wei Zhang received his Ph.D. degree from The University of Texas Graduate School of Biomedical Sciences. He joined the faculty of M. D. Anderson Cancer Center in 1994 where he is currently an Associate Professor and the Director of the Cancer Genomics Core Laboratory. Dr. Zhang's research programs include cancer genomics and informatics, tumor suppressor genes, and signal transduction studies. Dr. Zhang is an Associate Editor for American Association of Cancer Research journal Clinical Cancer Research, and serves on editorial board of journals Cancer Biology and Therapy, Histology and Histopathology, International Journal of Oncology, BMC Genomics, Technology in Cancer Research and Treatment, and Molecular Cancer.

Inhaltsangabe

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.