- Hardcover: 416 pages
- Publisher: MIT Press; illustrated edition edition (13 Aug 2004)
- Language English
- ISBN-10: 0262195097
- ISBN-13: 978-0262195096
- Product Dimensions: 26.2 x 20.9 x 2.6 cm
- Amazon Bestsellers Rank: 1,640,648 in Books (See Top 100 in Books)
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"This timely collection will be an asset to anyone working with microarray data, and those involved with computational biology more generally should be aware of it."--Jun Liu, Professor of Statistics, Harvard University
Product Description
Modern machine learning techniques are proving to be extremely valuable for the analysis of data in computational biology problems. One branch of machine learning, kernel methods, lends itself particularly well to the difficult aspects of biological data, which include high dimensionality (as in microarray measurements), representation as discrete and structured data (as in DNA or amino acid sequences), and the need to combine heterogeneous sources of information. This book provides a detailed overview of current research in kernel methods and their applications to computational biology.Following three introductory chapters -- an introduction to molecular and computational biology, a short review of kernel methods that focuses on intuitive concepts rather than technical details, and a detailed survey of recent applications of kernel methods in computational biology -- the book is divided into three sections that reflect three general trends in current research. The first part presents different ideas for the design of kernel functions specifically adapted to various biological data; the second part covers different approaches to learning from heterogeneous data; and the third part offers examples of successful applications of support vector machine methods.
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2 of 2 people found the following review helpful
diverse examples
4 July 2006
Format:Hardcover
The book is recognition of the fact that computational biology is only now starting to emerge as an important scientific discipline in its own right. The book addresses 2 audiences whose research intersects. One is those doing other computational work and who perhaps already use these kernel methods, and who are unaware of issues in biology that need to be studied. While the other is those already in computational biology, but who have never used kernel methods. Essentially, the early chapters address these needs.
Then the bulk of the book gives examples where kernel methods are already being used in computational biology. The diversity of the examples should prove inspiring to some readers.
The book also goes somewhat briefly into using support vector machines. If this interests you, try consulting "Support Vector Machines for Pattern Classification" by S Abe, Springer 2005, 1-85233-929-2. It has a fuller treatment of the idea.
Then the bulk of the book gives examples where kernel methods are already being used in computational biology. The diversity of the examples should prove inspiring to some readers.
The book also goes somewhat briefly into using support vector machines. If this interests you, try consulting "Support Vector Machines for Pattern Classification" by S Abe, Springer 2005, 1-85233-929-2. It has a fuller treatment of the idea.
Kernal Methods in Biology
9 Jan 2007
Format:Hardcover
Good Book and useful for research and as a course work
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