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

Authors:
Jason Wang;Stephen Griesmer;Miguel Cervantes-Cervantes;Stephen J. Griesmer;Yang Song;Jason T. L. Wang
Affiliations:
Bioinformatics Program and Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey 07102, USA.;Bioinformatics Program and Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey 07102, USA.;Department of Biological Sciences, Rutgers University, Newark, New Jersey 07102, USA.;Bioinformatics Program, New Jersey Institute of Technology, Newark, NJ 07102, USA.;Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA.;Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2011

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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.