Kernel design for RNA classification using Support Vector Machines

Document Type

Article

Publication Date

1-1-2006

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 this paper 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. © 2006 Inderscience Enterprises Ltd.

Identifier

34547457367 (Scopus)

Publication Title

International Journal of Data Mining and Bioinformatics

External Full Text Location

https://doi.org/10.1504/IJDMB.2006.009921

e-ISSN

17485681

ISSN

17485673

PubMed ID

18402042

First Page

57

Last Page

76

Issue

1

Volume

1

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