Statistical Bioinformatics: With R

Paperback | October 30, 2017

bySunil K. Mathur

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Designed for a one or two semester senior undergraduate or graduate bioinformatics course, Statistical Bioinformatics 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 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. Ancillary list: * Online ISM- http://textbooks.elsevier.com/web/manuals.aspx?isbn=9780123751041 * Companion Website w/ R code and Ebook- http://textbooks.elsevier.com/web/manuals.aspx?isbn=9780123751041 * Powerpoint slides- http://textbooks.elsevier.com/web/Manuals.aspx?isbn=9780123751041 Integrates biological, statistical and computational concepts Inclusion of R and SAS code Provides coverage of complex statistical methods in context with applications in bioinformatics Exercises and examples aid teaching and learning presented at the right level Bayesian methods and the modern multiple testing principles in one convenient book

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From the Publisher

Designed for a one or two semester senior undergraduate or graduate bioinformatics course, Statistical Bioinformatics 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 code as well as the devel...

Format:PaperbackDimensions:336 pages, 8.75 × 6.35 × 0.68 inPublished:October 30, 2017Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0128101865

ISBN - 13:9780128101865

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Table of Contents

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