A new kernel method for RNA classification

Xiaoming Wu, Jason Wang, Katherine G. Herbert

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - Sixth IEEE Symposium on BioInformatics and BioEngineering, BIBE 2006
Pages201-208
Number of pages8
DOIs
StatePublished - Dec 1 2006
Event6th IEEE Symposium on BioInformatics and BioEngineering, BIBE 2006 - Arlington, VA, United States
Duration: Oct 16 2006Oct 18 2006

Other

Other6th IEEE Symposium on BioInformatics and BioEngineering, BIBE 2006
CountryUnited States
CityArlington, VA
Period10/16/0610/18/06

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Computer Science Applications
  • Information Systems

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