CLASSIFICATION OF RNA SEQUENCES WITH SUPPORT VECTOR MACHINES

Document Type

Syllabus

Publication Date

1-1-2007

Abstract

Support vector machines (SVMs) are a state-of-the-art machine learning tool widely used in speech recognition, image processing and biological sequence analysis. An essential step in SVMs is to devise a kernel function to compute the similarity between two data points. In this chapter we review recent advances of using SVMs for RNA classification. In particular we present a new kernel that takes advantage of both global and local structural information in RNAs and uses the information together to classify RNAs. Experimental results demonstrate the good performance of the new kernel and show that it outperforms existing kernels when applied to classifying non-coding RNA sequences.

Identifier

85196442230 (Scopus)

ISBN

[9789812707802, 9789812708892]

Publication Title

Analysis of Biological Data A Soft Computing Approach

External Full Text Location

https://doi.org/10.1142/9789812708892_0004

First Page

85

Last Page

108

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