Prediction of small non-coding RNA in bacterial genomes using support vector machines

Tzu Hao Chang, Li Ching Wu, Jun Hong Lin, Hsien Da Huang, Baw Jhiune Liu, Kuang Fu Cheng, Jorng Tzong Horng

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Small non-coding RNA genes have been shown to play important regulatory roles in a variety of cellular processes, but prediction of non-coding RNA genes is a great challenge, using either an experimental or a computational approach, due to the characteristics of sRNAs, which are that sRNAs are small in size, are not translated into proteins and show variable stability. Most known sRNAs have been identified in Escherichia coli and have been shown to be conserved in closely related organisms. We have developed an integrative approach that searches highly conserved intergenic regions among related bacterial genomes for combinations of characteristics that have been extracted from known E. coli sRNA genes. Support vector machines (SVM) were then used with these characteristics to predict novel sRNA genes.

Original languageEnglish
Pages (from-to)5549-5557
Number of pages9
JournalExpert Systems with Applications
Volume37
Issue number8
DOIs
Publication statusPublished - Aug 2010
Externally publishedYes

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Keywords

  • Bioinformatics
  • Expert systems
  • Machine learning
  • Non-coding RNA
  • Support vector machines

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Engineering(all)

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