In silico prediction of noncoding RNAs using supervised learning and feature ranking methods

Stephen J. Griesmer, Miguel Cervantes-Cervantes, Yang Song, Jason T.L. Wang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

We propose here a new approach for ncRNA prediction. Our approach selects features derived from RNA folding programs and ranks these features using a class separation method that measures the ability of the features to differentiate between positive and negative classes. The target feature set comprising top-ranked features is then used to construct several classifiers with different supervised learning algorithms. These classifiers are compared to the same supervised learning algorithms with the baseline feature set employed in a state-of-the-art method. Experimental results based on ncRNA families taken from the Rfam database demonstrate the good performance of the proposed approach.

Original languageEnglish (US)
Pages (from-to)355-375
Number of pages21
JournalInternational Journal of Bioinformatics Research and Applications
Volume7
Issue number4
DOIs
StatePublished - Nov 2011

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics
  • Clinical Biochemistry
  • Health Information Management

Keywords

  • Classification and prediction
  • Feature generation and ranking
  • Noncoding RNA

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