Kernel Methods in Computational Biology / Edition 1

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Overview

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.

The MIT Press

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What People Are Saying

From the Publisher

"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

The MIT Press

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Product Details

  • ISBN-13: 9780262195096
  • Publisher: MIT Press
  • Publication date: 8/1/2004
  • Series: Computational Molecular Biology
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 416
  • Product dimensions: 8.00 (w) x 10.00 (h) x 1.00 (d)

Meet the Author

Bernhard Schölkopf is Professor and Director at the Max Planck Institute for
Biological Cybernetics in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel
Methods: Support Vector Learning
(1998), Advances in Large-Margin
Classifiers
(2000), and Kernel Methods in Computational
Biology
(2004), all published by the MIT Press.

Koji Tsuda is a Research Scientist at the Max Planck Institute and a Researcher at
AIST Computational Biology Research Center, Tokyo.

Jean-Philippe Vert is Researcher and Leader of the Bioinformatics Group at École des Mines de Paris.

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Table of Contents

1 A primer on molecular biology 3
2 A primer on kernel methods 35
3 Support vector machine applications in computational biology 71
4 Inexact matching string kernels for protein classification 95
5 Fast kernels for string and tree matching 113
6 Local alignment kernels for biological sequences 131
7 Kernels for graphs 155
8 Diffusion kernels 171
9 A kernel for protein secondary structure prediction 193
10 Heterogeneous data comparison and gene selection with kernel canonical correlation analysis 209
11 Kernel-based integration of genomic data using semidefinite programming 231
12 Protein classification via kernel matrix completion 261
13 Accurate splice site detection for Caenorhabditis elegans 277
14 Gene expression analysis : joint feature selection and classifier design 299
15 Gene selection for microarray data 319
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