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Bioinformatics Advance Access originally published online on August 31, 2006
Bioinformatics 2006 22(21):2590-2596; 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

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, USA
2 Computational Research Division, Lawrence Berkeley National Laboratory Berkeley, CA 94720, USA

*To whom correspondence should be addressed.

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 Escherichia 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.

Contact: srholbrook{at}lbl.gov

Supplementary information: Supplementary data are available at Bioinformatics online.

Received on May 30, 2006; revised on August 2, 2006; accepted on August 13, 2006

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[Abstract] [Full Text] [PDF]


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