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Statistical Bioinformatics: with R, by Sunil 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 & 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
- Sales Rank: #2203721 in Books
- Published on: 2010-01-26
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x .90" w x 7.40" l, 1.95 pounds
- Binding: Hardcover
- 336 pages
Review
"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
Most helpful customer reviews
38 of 40 people found the following review helpful.
A statistics textbook masquerading as a statistical bioinformatics textbook, forget about R
By Jeremy Leipzig
This is a statistics and probability textbook with some of the author's limited exposure to bioinformatics thrown in - the bioinformatics material is absurdly narrow in scope and most of the R code might as well be omitted it is so worthless.
The treatment of statistics is decent - a thorough overview of probability, distributions, inference, and Bayesian statistics is presented. There are so many summation and integral symbols in here it will make your eyes glaze over. At its core this might be a decent statistics textbook. Most of the examples seem fairly generic - such that biological concepts were placed in phrases where terms from the social sciences or engineering could have been used just as easily.
The R code is woefully repetitious, or in other cases so elementary it just takes up space. For example,
>E1RE1GE1BE1NE1N
[1] 14 -> gee thanks!
Perhaps due to the author's research interests this is a very microarray-centric textbook, and would have been more relevant 6 years ago. I wish authors would do a brief metasearch before sitting down to organize a textbook. There are probably 1000+ bioinformatics papers in 2009 dealing in the statistics of genome wide association analysis, and a handful of papers on MCMC or using the Metropolis Hastings algorithm. Yes parameter estimation deserves attention but not 100% weight. Protein microarrays, many of whose manufacturers have gone belly up, gets its own section for some reason. Next generation sequencing gets no exposure whatsoever (the copyright on this book is 2010 people!). Statistical genetics - QTLs, linkage disequilibrium - nothing. I'm pretty sure there is some sophisticated statistics involved in BLAST alignments and estimating homology but it is not mentioned here. The author instead focused on explaining stuff like "single fractal analysis" for binding kinetics (isn't that biochemistry?)
All the color figures are at the back, and consist of low-resolution gifs obviously taken from the web. The author did not contribute much in way of figures to aid understanding of posterior probability or Markov Chains lest it take space from the umpteenth formula or R "print" statement.
This hardcover is an expensive way to learn statistics and the author's myopic view of bioinformatics and weak code will not serve students well in their careers.
By the way, don't think I didn't notice the abrupt influx of suspicious one-off positive reviews in mid-February. Whoever orchestrated this scheme should bear in mind this kind of shameless shilling would be frowned upon by most universities.
12 of 14 people found the following review helpful.
a disappointing book
By Sergey Prykhozhij
I would like to learn more of statistical bioinformatics and R programming and therefore expected this book to be published. Unfortunately, reading this book turned to be a disappointing experience. First of all, the presentation is pretty uneven: a lot of things are very basic, whereas some mathematics is much more difficult and requires good math training. Second, R programming is not at all interesting in this book and one cannot learn useful skills in this area. Another problem is that the book has a rather limited scope not highlighting many important areas of statistical bioinformatics of current interest. Finally, there are some factual mistakes in the theory, e.g. alternative hypothesis in ANOVA does not test that all the means are unequal, that is, no two or more means are equal to each other. To conclude, I appreciate that the author put a lot of work into this book, but the result still needs a lot to be useful and popular with bioinformatics students.
3 of 7 people found the following review helpful.
Useful book
By Sidd
I find this book useful as it takes broad view of bioinformatics applications and development of advanced methodology including Bayesian and Markov models. This book also includes variety of applications in different biomedical and genomic areas, including sequence analysis, location of recombinant breakpoints, complex designs, gene clustering and microarray.
The language of book is lucid and quite explains the basics of R code in reference to high-dimensional problems in the area of bioinformatics. I would like to thank the author for this wonderful book for beginners like me. I will recommend it.
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