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Bioinformatics Advance Access published online on March 14, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn089
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Biological sequence classification utilizing positive and unlabeled data

Yuanyuan Xiao and Mark R. Segal

Department of Epidemiology and Biostatistics, Center for Bioinformatics and Molecular Biostatistics, University of California, 185 Berry Street, Lobby 4, Suite 5700, San Francisco, CA 94107, USA.

To whom correspondence should be addressed. Mark R. Segal, E-mail: mark{at}biostat.ucsf.edu


  Abstract

Motivation: In the genomics setting, an increasingly common data configuration consists of a small set of sequences possessing a targeted property (positive instances) amongst a large set of sequences for which class membership is unknown (unlabeled instances). Traditional two-class classification methods do not effectively handle such data.

Results: Here, we develop a novel method, Likely Positive-Iterative Classification (LP-IC), for this problem and contrast its performance with the few existing methods, most of which were devised and utilized in the text classification context. LPIC employs an iterative classification scheme and introduces a class dispersion measure, adopted from unsupervised clustering approaches, to monitor the model selection process. Using two case studies – prediction of HLA binding, and alternative splicing conservation between human and mouse – we show that LP-IC provides superior performance to existing methodologies in terms of: (i) combined accuracy and precision in positive identification from the unlabeled set; and (ii) predictive performance of the resultant classifiers on independent test data.

Contact: mark{at}biostat.ucsf.edu

Associate Editor: Dr. Limsoon Wong

Received on December 18, 2007; revised on January 31, 2008; accepted on March 4, 2008

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