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This article is part of the supplement: Selected articles from the Eighth Asia-Pacific Bioinformatics Conference (APBC 2010)

Open Access Research

Active machine learning for transmembrane helix prediction

Hatice U Osmanbeyoglu1, Jessica A Wehner2, Jaime G Carbonell3 and Madhavi K Ganapathiraju14*

Author Affiliations

1 Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

2 Department of Mathematics, University of North Carolina, Chapel Hill, NC, USA

3 Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA

4 Intelligent Systems Program, University of Pittsburgh School of Art and Sciences, Pittsburgh, PA, USA

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BMC Bioinformatics 2010, 11(Suppl 1):S58  doi:10.1186/1471-2105-11-S1-S58

Published: 18 January 2010

Abstract

Background

About 30% of genes code for membrane proteins, which are involved in a wide variety of crucial biological functions. Despite their importance, experimentally determined structures correspond to only about 1.7% of protein structures deposited in the Protein Data Bank due to the difficulty in crystallizing membrane proteins. Algorithms that can identify proteins whose high-resolution structure can aid in predicting the structure of many previously unresolved proteins are therefore of potentially high value. Active machine learning is a supervised machine learning approach which is suitable for this domain where there are a large number of sequences but only very few have known corresponding structures. In essence, active learning seeks to identify proteins whose structure, if revealed experimentally, is maximally predictive of others.

Results

An active learning approach is presented for selection of a minimal set of proteins whose structures can aid in the determination of transmembrane helices for the remaining proteins. TMpro, an algorithm for high accuracy TM helix prediction we previously developed, is coupled with active learning. We show that with a well-designed selection procedure, high accuracy can be achieved with only few proteins. TMpro, trained with a single protein achieved an F-score of 94% on benchmark evaluation and 91% on MPtopo dataset, which correspond to the state-of-the-art accuracies on TM helix prediction that are achieved usually by training with over 100 training proteins.

Conclusion

Active learning is suitable for bioinformatics applications, where manually characterized data are not a comprehensive representation of all possible data, and in fact can be a very sparse subset thereof. It aids in selection of data instances which when characterized experimentally can improve the accuracy of computational characterization of remaining raw data. The results presented here also demonstrate that the feature extraction method of TMpro is well designed, achieving a very good separation between TM and non TM segments.