In silico prediction of noncoding RNAs using supervised learning and feature ranking methods
Document Type
Article
Publication Date
1-1-2011
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. Copyright © 2011 Inderscience Enterprises Ltd.
Identifier
84863354009 (Scopus)
Publication Title
International Journal of Bioinformatics Research and Applications
External Full Text Location
https://doi.org/10.1504/IJBRA.2011.043768
e-ISSN
17445493
ISSN
17445485
First Page
355
Last Page
375
Issue
4
Volume
7
Recommended Citation
Griesmer, Stephen J.; Cervantes-Cervantes, Miguel; Song, Yang; and Wang, Jason T.L., "In silico prediction of noncoding RNAs using supervised learning and feature ranking methods" (2011). Faculty Publications. 11578.
https://digitalcommons.njit.edu/fac_pubs/11578
