CLASSIFICATION OF RNA SEQUENCES WITH SUPPORT VECTOR MACHINES

Jason T.L. Wang, Xiaoming Wu

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish (US)
Title of host publicationAnalysis of Biological Data
Subtitle of host publicationA Soft Computing Approach
PublisherWorld Scientific Publishing Co.
Pages85-108
Number of pages24
ISBN (Electronic)9789812708892
ISBN (Print)9789812707802
DOIs
StatePublished - Jan 1 2007
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Agricultural and Biological Sciences
  • General Biochemistry, Genetics and Molecular Biology

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