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Bioinformatics Advance Access published online on August 31, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl441
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received May 30, 2006
Revised August 2, 2006
Accepted August 13, 2006

Article

PSoL: a positive sample only learning algorithm for finding non-coding RNA genes

Chunlin Wang 1, Chris Ding 2, Richard F. Meraz 1, and Stephen R. Holbrook 1 *

1 Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720
2 Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720

* To whom correspondence should be addressed.
Stephen R. Holbrook, E-mail: srholbrook{at}lbl.gov


  Abstract

Motivation: Small non-coding RNA (ncRNA) genes play important regulatory roles in a variety of cellular processes. However, detection of ncRNA genes is a great challenge to both experimental and computational approaches. In this study, we describe a new approach called positive sample only learning (PSoL) to predict ncRNA genes in the E. coli genome. Although PSoL is a machine learning method for classification, it requires no negative training data, which, in general, is hard to define properly and affects the performance of machine learning dramatically. In addition, using the support vector machine (SVM) as the core learning algorithm, PSoL can integrate many different kinds of information to improve the accuracy of prediction. Besides the application of PSoL for predicting ncRNAs, PSoL is applicable to many other bioinformatics problems as well.

Results: The PSoL method is assessed by 5-fold cross-validation experiments which show that PSoL can achieve about 80% accuracy in recovery of known ncRNAs. We compared PSoL predictions with five previously published results. The PSoL method has the highest percentage of predictions overlapping with those from other methods.

Associate Editor: Martin Bishop
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