Computational and statistical methods in bioinformatics

Authors:
Tatsuya Akutsu
Affiliations:
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji-city, Kyoto, Japan
Venue:
AM'03 Proceedings of the Second international conference on Active Mining
Year:
2003

Citing 7
Cited 0

Support-Vector Networks

Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Diffusion Kernels on Graphs and Other Discrete Input Spaces

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Extensions of marginalized graph kernels

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Mismatch string kernels for discriminative protein classification

Bioinformatics
Optimizing substitution matrices by separating score distributions

Bioinformatics
Protein homology detection using string alignment kernels

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Many computational and statistical methods have been developed and applied in bioinformatics. Recently, new approaches based on support vector machines have been developed. Support vector machines provide a way of combining computational methods and statistical methods. After overviewing fundamental computational and statistical methods in bioinformatics, this paper surveys how these methods are used with support vector machines in order to analyze biological sequence data. This paper also overviews a method to handle chemical structures using support vector machines.