Kernel Methods in Computational Biology

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Bernhard Schölkopf, Koji Tsuda, Jean-Philippe Vert
MIT Press, 2004 - Computers - 400 pages
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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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Contents

A Primer on Molecular Biology
3
A Primer on Kernel Methods
35
Support Vector Machine Applications in Computational Biology
71
KERNELS FOR BIOLOGICAL DATA
93
Inexact Matching String Kernels for Protein Classification
95
Fast Kernels for String and Tree Matching
113
Local Alignment Kernels for Biological Sequences
131
Kernels for Graphs
155
Heterogeneous Data Comparison and Gene Selection with Kernel Canonical Correlation Analysis
209
KernelBased Integration of Genomic Data Using Semidefinite Programming
231
Protein Classification via Kernel Matrix Completion
261
ADVANCED APPLICATION OF SUPPORT VECTOR MACHINES
275
Accurate Splice Site Detection for Caenorhabditis elegans
277
Gene Expression Analysis Joint Feature Selection and Classifier Design
299
Gene Selection for Microarray Data
319
References
357

Diffusion Kernels
171
A Kernel for Protein Secondary Structure Prediction
193
DATA FUSION WITH KERNEL METHODS
207
Contributors
391
Index
397
Copyright

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About the author (2004)

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

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