Kernel Methods in Computational Biology (Computational Molecular Biology) (Hardcover)

by Bernhard Schölkopf (Editor), Koji Tsuda (Editor), Jean-Philippe Vert (Editor)

Editorial Reviews

Review
"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

"This unique collection admirably covers the topic of using kernel methods to study biological data, providing up-to-date treatment of work in the field."
—Junhyong Kim, Department of Biology, University of Pennsylvania

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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Product Details

• Hardcover: 410 pages
• Publisher: The MIT Press (August 1, 2004)
• Language: English
• ISBN-10: 0262195097
• ISBN-13: 978-0262195096
• Product Dimensions: 10 x 8.3 x 1.1 inches
• Shipping Weight: 2.3 pounds (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars See all reviews (2 customer reviews)
• Amazon.com Sales Rank: #1,058,974 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

2 of 2 people found the following review helpful:

4.0 out of 5 stars diverse examples, July 4, 2006

By	W Boudville (Terra, Sol 3) - See all my reviews (TOP 50 REVIEWER)    (REAL NAME)

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.

4.0 out of 5 stars Kernal Methods in Biology, January 9, 2007

By	DrChase "drvchase" (INDIA) - See all my reviews

Good Book and useful for research and as a course work