Statistical Bioinformatics with R - Sunil K Mathur

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Auteur(s) : Sunil K Mathur

Editeur : Elsevier Science

Langue : Anglais

Parution : 01/01/2010

Format : Moyen, de 350g à 1kg

Nombre de pages : 336

Expédition : 882

Dimensions : 24.5 x 19.7 x 3.4

ISBN : 9780123751041

Résumé :

Statistical Bioinformatics provides a balanced treatment of statistical theory in the context of bioinformatics applications.

Designed for a one or two semester senior undergraduate or graduate bioinformatics course, the text takes a broad view of the subject - not just gene expression and sequence analysis, but a careful balance of statistical theory in the context of bioinformatics applications.

The inclusion of R & SAS code as well as the development of advanced methodology such as Bayesian and Markov models provides students with the important foundation needed to conduct bioinformatics.

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Biographie:
Dr. Sunil Mathur is a distinguished professor of Biostatistics at Weill Cornell Medical College and a full member of both the Houston Methodist Academic Institute and the Houston Methodist Neal Cancer Center. He serves as the Director of Biostatistics and Co-Director of the Biostatistics and Bioinformatics Shared Resources at the Houston Methodist Neal Cancer Center and the Houston Methodist Research Institute. Dr. Mathur has previously held several key positions: he was a Professor and Chair of the Department of Mathematics and Statistics and Assistant to the Dean for Research Development at Texas A&M University-Corpus Christi...

Sommaire:

1. Introduction
2. Genomics
3. Probability and Statistical Theory
4. Special Distributions, Properties and Applications
5. Statistical Inference and Applications6. Nonparametric Statistics
7. Bayesian Statistics
8. Markov Chain, Monte Carlo
9. Analysis of Variance
10. Design of Experiments
11. Multiple Testing of Hypotheses

Students and biologists who want to specialize in the fast-paced field of bioinformatics should read this book. Mathur brings together a comprehensive and very practical view of the field. He combines sufficient mathematical proofs with hints and suggestions, and provides many real examples taken directly from the genetics, proteomics, and molecular biology fields.Many other bioinformatics topics--for example, clustering algorithms, specialized R packages, or the challenges of analyzing mass-spectrometry data--are only alluded to and not covered fully in the book. However, in its entirety, this is a very useful, clearly written introduction to statistical bioinformatics with R. It contains many real examples, and would be a help to those starting out in the field.--Computing Reviews.com