Kernel Methods in Computational Biology

Bernhard Scholkopf, Koji Tsuda, Jean-Philippe Vert

400 pages, parution le 04/10/2004

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Résumé

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.

L'auteur - Koji Tsuda

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

L'auteur - Jean-Philippe Vert

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

Sommaire

Introduction
- A Primer on Molecular Biology
- A Primer on Kernel Methods
- Support Vector Machine Applications in Computational Biology
Kernels for biological data
- Inexact Matching String Kernels for Protein Classification
- Fast Kernels for String and Tree Matching
- Local Alignment Kernels for Biological Sequences
- Kernels for Graphs
- Diffusion Kernels
- A Kernel for Protein Secondary Structure Prediction
Data fusion with kernel methods
- Heterogeneous Data Comparison and Gene Selection with Kernel Canonical Correlation Analysis
- Kernel-Based Integration of Genomic Data Using Semidefinite Programming
- Protein Classification via Kernel Matrix Completion
Advanced application of support vector machines
- Accurate Splice Site Detection for Caenorhabditis elegans
- Gene Expression Analysis: Joint Feature Selection and Classifier Design
- Gene Selection for Microarray Data

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Caractéristiques techniques

	PAPIER
Éditeur(s)	The MIT Press
Auteur(s)	Bernhard Scholkopf, Koji Tsuda, Jean-Philippe Vert
Parution	04/10/2004
Nb. de pages	400
Format	20,5 x 26
Couverture	Broché
Poids	1051g
Intérieur	Noir et Blanc
EAN13	9780262195096
ISBN13	978-0-262-19509-6