Statistical Bioinformatics with R,
Edition 1Editors: By Sunil K. Mathur
Ways Of Reading
-
This e-publication is accessible to the full extent that the file format and types of content allow, on a specific reading device, by default, without necessarily including any additions such as textual descriptions of images or enhanced navigation.
Navigation
-
The contents of the PDF have been tagged to permit access by assistive technologies as per PDF-UA-1 standard.
-
Page breaks included from the original print source
Additional Accessibility Information
-
All (or substantially all) textual matter is arranged in a single logical reading order (including text that is visually presented as separate from the main text flow, e.g., in boxouts, captions, tables, footnotes, endnotes, citations, etc.). Non-textual content is also linked from within this logical reading order. (Purely decorative non-text content can be ignored).
-
The language of the text has been specified (e.g., via the HTML or XML lang attribute) to optimise text-to-speech (and other alternative renderings), both at the whole document level and, where appropriate, for individual words, phrases or passages in a different language.
Conformance
-
The publication was certified on 20250728
-
Accessibility addendum
-
For detailed accessibility information, see Elsevier’s website at https://www.elsevier.com/about/accessibility
-
For queries regarding accessibility information, contact [email protected]
Note
-
This product relies on 3rd party tooling which may impact the accessibility features visible in inspection copies. All accessibility features mentioned would be present in the purchased version of the title.
Description
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.
Key Features
- Integrates biological, statistical and computational concepts
- Inclusion of R & 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
About the author
By Sunil K. Mathur, Director, Statistical Computing and Consulting Center University of Mississippi, Oxford, USA
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
Title Reviews
Baxevanis & Ouellette, Bioinformatics: A Proactical Guide to the Analysis of Genes and Proteins, 2E, Wiley, ISBN: 9780471478782, 540 pp, HB, $103.50, 2004
Ewens & Grant, Statistical Methods in Bioinformatics: An Introduction, Springer, ISBN: 9780387400822, 588 pp, HB, $94.95, 2005
Durbin, et al, Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, CUP, ISBN: 9780521629713, 356 pp, PB, $60.00, 1999
Polanski & Kimmel, Bioinformatics, Springer, ISBN: 9783540241669, 376 pp, HB, $69.95, 2007
- 01~Chapter_01.zip
- 02~Chapter_03.zip
- 03~Chapter_04.zip
- 04~Chapter_05.zip
- 05~Chapter_06.zip
- 06~Chapter_07.zip
- 07~Chapter_08.zip
- 08~Chapter_09.zip
- 09~Chapter_10.zip
- 10~Chapter_11.zip
- Default.aspx
- Exercises_to_accompany_the_book_Statistical_Bioinformatics_with_R.pdf
- Instructors_solution_manual_for_the_book_Statistical_Bioinformatics_with_R.pdf
- PowerPoint
- R_Files.zip
- default.asp
- intro.xml
- intro_old.xml
- old_intro.xml