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

Authors: Stephen J. Griesmer; Miguel Cervantes-Cervantes; Yang Song; Jason T.L. Wang

Addresses: Bioinformatics Program, New Jersey Institute of Technology, Newark, NJ 07102, USA. ' Department of Biological Sciences, Rutgers University, Newark, New Jersey 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

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.

Keywords: noncoding RNA; classification; ncRNA prediction; feature generation; feature ranking; supervised learning; bioinformatics.

DOI: 10.1504/IJBRA.2011.043768

International Journal of Bioinformatics Research and Applications, 2011 Vol.7 No.4, pp.355 - 375

Available online: 14 Nov 2011

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