Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health) Reviewed
USUALLY I am very positive in my responses. But this gibberish:
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COPYRIGHTED MATERIAL
Warren J. Ewens, Gregory Grant. Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health). (Springer, 2005). Page 231.
is what you get when you (pay for) the digital copy and want to use your (secret number of) copy privileges. Your print privileges – to print out the page – are limited as well and you do not know ahead of time what that limit is. It seems to be 0. Which should not be sold as a print privilege. The annotations come out in this same weird encoding and the “Report a Problem” link has been irritating me since I first tried April 17 when I purchased this article. I wanted to report that the text is dim and fuzzy, very difficult for reading online, so I filled out the “Report a problem” form. I spent time filling it out. The response was that “We know this doesn’t work and we are working on it and try again later”. That was April 17. Still happening. There is no place to rate the digital service.
I gave up and wrote regular Amazon customer service.
Amazon customer service refunded the price of the digital subscription.
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Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health) Overview
Advances in computers and biotechnology have had an immense impact on the biomedical fields, with broad consequences for humanity. Correspondingly, new areas of probability and statistics are being developed specifically to meet the needs of this area. There is now a necessity for a text that introduces probability and statistics in the bioinformatics context. This book also describes some of the main statistical applications in the field, including BLAST, gene finding, and evolutionary inference, much of which has not yet been summarized in an introductory textbook format. This book grew out of a need to teach bioinformatics to graduate students at the University of Pennsylvania. At the same time however, it is organized to appeal to a wider audience. In particular it should appeal to any biologist or computer scientist who wants to know more about the statistical methods of the field, as well as to a trained statistician who wishes to become involved in bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level, and will be accessible to students who have only had introductory calculus and linear algebra. Later chapters are immediately accessible to the trained statistician. Only a basic understanding of biological concepts is assumed, and all concepts are explained when used or can be understood from the context. Several chapters contain material independent of that in other chapters, so that the reader interested in certain areas can proceed directly to those areas. Warren Ewens is Professor of Biology at the University of Pennsylvania. He is the author of two books, Population Genetics and Mathematical Population Genetics, and has served on the editorial boards of Theoretical Population Biology, GENETICS, Proceeding of the Royal Society B and SIAM Journal in Mathematical Biology. He was recently awarded the Gold Medal of the Australian Statistical Society and elected as Fellow of the Royal Society. His research interests are in evolutionary population genetics, linkage analysis for human diseases, and bioinformatics. Gregory Grant is a bioinformatics researcher at the University of Pennsylvania in the Computational Biology and Informatics Laboratory (CBIL), where he has been since 1998. In 1995 he received a Ph.D. in Mathematics from the University of Maryland and in 1999 a Masters in Computer Science from the University of Pennsylvania. His research interests are in bioinformatics in general and in particular in the statistical analysis of gene expression data and significance testing methods for IBD-mapping.
Best Buy Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health) :Customer Reviews
A good read, but only if you have adequate probability and statistics background – TFKhang –
This is a useful book for people who have some background in probability and statistics to understand methods in bioinformatics. The chapter on BLAST theory is useful, as not too many books talk about it. Newcomers into the field who have absolutely no math background may find it hard to understand, though – definitely not a book for the beginner.
Lots of material made accessible – Student T – East Coast, USA
I’m a Statistics PhD student so you can condition on my prior to get at what’s really going on with this book.
Bioinformatics is a departure from “regular” statistics and looks awfully messy at first pass. The sorts of assumptions one typically makes in other areas of statistical inference are patently false, so new techniques and intuitions have to be built up in order to attack these kinds of problems. This book does an excellent job of balancing the technical details with the necessary intuitions so one can really get a firm grasp on what’s going on.
I wouldn’t recommend this book to someone who hasn’t done statistics at at least an advanced undergrad level (e.g., comfortable with Probability at the Ross-level and Statistical Inference at the Casella/Berger-level). But for people really interested in the material and coming from a solid statistical background the book is an excellent resource.
I would also strongly recommend it to teach out of.
Most Elegant Account of Bioinformatics – RandomThoughts – DC
I was impressed with the 1st edition of this book for its most comprehensive and elegant of statistical techniques in bioinformatics. The book is slightly below the level of the now classic M S Waterman (1995)book:Introduction to Computational Biology: Maps, Sequences and Genomes (Interdisciplinary Statistics). But this book is more update in some areas and has much more background materials on probability and statistics, which should provide a solid basis for understanding bioinformatics. Its pedagorical sense is unparalleled. It would make a very good choice for a stat/math oriented introduction to bioinformatics (as opposed to algorithimc/database oriented approach in cs).
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I enjoyed reading this book for my class. It has short chapters with quick review definitions with great explanations and examples.
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