Kernel Methods in Computational Biology

by Scholkopf, Bernhard

• ISBN13: 9780262195096
• ISBN10: 0262195097
• Edition: 1st
• Format: Hardcover
• Copyright: 2004-08-01
• Publisher: MIT PRESS

Summary

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.

Table of Contents

Preface	vii
I INTRODUCTION	1	(92)
A Primer on Molecular Biology	3	(32)
Alexander Zien
A Primer on Kernel Methods	35	(36)
Jean-Philippe Vert
Koji Tsuda
Bernhard Scholkopf
Support Vector Machine Applications in Computational Biology	71	(22)
William Stafford Noble
II KERNELS FOR BIOLOGICAL DATA	93	(114)
Inexact Matching String Kernels for Protein Classification	95	(18)
Christina Leslie
Rui Kuang
Eleazar Eskin
Fast Kernels for String and Tree Matching	113	(18)
S.V.N. Vishwanathan
Alexander Johannes Smola
Local Alignment Kernels for Biological Sequences	131	(24)
Jean-Philippe Vert
Hiroto Saigo
Tatsuya Akutsu
Kernels for Graphs	155	(16)
Hisashi Kashima
Koji Tsuda
Akihiro Inokuchi
Diffusion Kernels	171	(22)
Risi Kondor
Jean-Philippe Vert
A Kernel for Protein Secondary Structure Prediction	193	(14)
Yann Guermeur
Alain Lifchitz
Regis Vert
III DATA FUSION WITH KERNEL METHODS	207	(68)
Heterogeneous Data Comparison and Gene Selection with Kernel Canonical Correlation Analysis	209	(22)
Yoshihiro Yamanishi
Jean-Philippe Vert
Minoru Kanehisa
Kernel-Based Integration of Genomic Data Using Semidefinite Programming	231	(30)
Gert R. G. Lanckriet
Nello Cristianini
Michael I. Jordan
William Stafford Noble
Protein Classification via Kernel Matrix Completion	261	(14)
Taishin Kin
Tsuyoshi Kato
Koji Tsuda
IV ADVANCED APPLICATION OF SUPPORT VECTOR MACHINES	275	(82)
Accurate Splice Site Detection for Caenorhabditis elegans	277	(22)
Gunnar Ratsch
Soren Sonnenburg
Gene Expression Analysis: Joint Feature Selection and Classifier Design	299	(20)
Balaji Krishnapuram
Lawrence Carin
Alexander Hartemink
Gene Selection for Microarray Data	319	(38)
Sepp Hochreiter
Klaus Obermayer
References	357	(34)
Contributors	391	(6)
Index	397