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Bioinformatics Advance Access published online on October 18, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti721
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received August 14, 2005
Revised October 6, 2005
Accepted October 14, 2005

Article

Prediction of protein-protein interactions using random decision forest framework

Xue-wen Chen 1* and Mei Liu 1

1 Bioinformatics and Computational Life-Sciences Laboratory, ITTC, Department of Electrical Engineering and Computer Science, The University of Kansas, 1520 West 15th Street, Lawrence KS 66045, USA

* To whom correspondence should be addressed.
Xue-wen Chen, E-mail: xwchen{at}ku.edu


  Abstract

Motivation: Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Domains are the building blocks of proteins; therefore, proteins are assumed to interact as a result of their interacting domains. Many domain based models for protein interaction prediction have been developed, and preliminary results have demonstrated their feasibility. Most of the existing domain-based methods, however, consider only single domain pairs (one domain from one protein) and assume independence between domain-domain interactions.

Results: In this paper, we introduce a domain based Random Forest of Decision Trees to infer protein interactions. Our proposed method is capable of exploring all possible domain interactions and making predictions based on all the protein domains. Experimental results on Saccharomyces Cerevisiae dataset demonstrate that our approach can predict protein-protein interactions with higher sensitivity (79.78%) and specificity (64.38%) compared to the Maximum Likelihood approach. Furthermore, our model can be used to infer interactions not only for single domain pairs but also for multiple domain pairs.

Availability: Source code is written in Java and is available upon request from the authors.

Supplementary: http://www.ittc.ku.edu/~xwchen/PPI/random_forest_PPI.


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