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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 learning for human protein-protein interaction prediction

Thahir P Mohamed12, Jaime G Carbonell3 and Madhavi K Ganapathiraju12*

Author Affiliations

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

2 Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA

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

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BMC Bioinformatics 2010, 11(Suppl 1):S57  doi:10.1186/1471-2105-11-S1-S57

Published: 18 January 2010

Abstract

Background

Biological processes in cells are carried out by means of protein-protein interactions. Determining whether a pair of proteins interacts by wet-lab experiments is resource-intensive; only about 38,000 interactions, out of a few hundred thousand expected interactions, are known today. Active machine learning can guide the selection of pairs of proteins for future experimental characterization in order to accelerate accurate prediction of the human protein interactome.

Results

Random forest (RF) has previously been shown to be effective for predicting protein-protein interactions. Here, four different active learning algorithms have been devised for selection of protein pairs to be used to train the RF. With labels of as few as 500 protein-pairs selected using any of the four active learning methods described here, the classifier achieved a higher F-score (harmonic mean of Precision and Recall) than with 3000 randomly chosen protein-pairs. F-score of predicted interactions is shown to increase by about 15% with active learning in comparison to that with random selection of data.

Conclusion

Active learning algorithms enable learning more accurate classifiers with much lesser labelled data and prove to be useful in applications where manual annotation of data is formidable. Active learning techniques demonstrated here can also be applied to other proteomics applications such as protein structure prediction and classification.