A new kernel method for RNA classification

Document Type

Conference Proceeding

Publication Date

12-1-2006

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 Euclidean space. In this paper 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 with support vector machines. 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. © 2006 IEEE.

Identifier

34547482842 (Scopus)

ISBN

[0769527272, 9780769527277]

Publication Title

Proceedings Sixth IEEE Symposium on Bioinformatics and Bioengineering Bibe 2006

External Full Text Location

https://doi.org/10.1109/BIBE.2006.253335

First Page

201

Last Page

208

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