Data Analysis and Classification for Bioinformatics [Paperback] Author: | Language: English | ISBN:
0970029705 | Format: PDF, EPUB
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With the explosion of sequence data in public and private databases and the coming explosion of gene expression data in a similar vein, it is becoming increasingly important to understand how to apply well-established data analysis and data classification methods that have been developed in other fields to this field---to try to make sense of the data, to glean biological insights from it, to categorize the data, and to put all of these to good use in industrial applications.
This book introduces the main methods of data analysis and of data classification--as applied to sequence and gene expression analysis--to the biologist and to the computer scientist in this field. It contains material that is presently being taught by the author in the course Data Analysis, Modeling, and Visualization for Bioinformatics at the University of California, Santa Cruz Extension to workers in the biotechnology industry in Silicon Valley.
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- Paperback: 100 pages
- Publisher: Bioinformatics By the Bay; 1 edition (August 1, 2000)
- Language: English
- ISBN-10: 0970029705
- ISBN-13: 978-0970029706
- Product Dimensions: 7.4 x 9.4 inches
- Shipping Weight: 6.4 ounces
- Amazon Best Sellers Rank: #4,416,512 in Books (See Top 100 in Books)
Despite of an appearance of being mathematically sound, Jagota's book is full of hand-waving and imprecision. The author jumps from one topic to another using unexplained terminology (particularly in examples.) I also don't trust the author's attitude of commenting on the well established statistical terminology in a way that can confuse and mislead the reader. One example of such misleading comments is implying the existence of "symbolic random variable." In fact the "symbolic random variable" IS a "usual" random variable, as it should. But it is a minor problem comparing to the overall lack of precision, missing background explanations and often inadequate references.
In my judgement the book could be used by experienced bioinformaticians as a non-essential desk reference. Beginners and serious students should keep far away from it.
By A Customer
I'm a beginner to the field, but have decent mathematical background. This book reads more like a collection of teacher's notes than a clear guide to understanding the subject areas. It could use better organization such as placing clear explanations of where these algorithms should be used before describing them. The limited examples aren't always clear. The author is very terse. Sometimes this leaves a new reader without an explanation on part of a formula.
I don't know if there is a better book out there. However, this one could be vastly improved by having the author sit down with people trying to read it and incorporating their feedback. In conjunction with his class I can see how they might work together. Without a class to get further explanation, some of this material is very tough to understand.
By A Customer
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