Protein-protein interaction (PPI) extraction has been a focal point of many biomedical research and database curation tools. Both Active Learning and Semi-supervised SVMs have recently been applied to extract PPI automatically. In this paper, we explore combining the AL with the SSL to improve the performance of the PPI task. We propose a novel PPI extraction technique called PPISpotter by combining Deterministic Annealing-based SSL and an AL technique to extract protein-protein interaction. In addition, we extract a comprehensive set of features from MEDLINE records by Natural Language Processing (NLP) techniques, which further improve the SVM classifiers. In our feature selection technique, syntactic, semantic, and lexical properties of text are incorporated into feature selection that boosts the system performance significantly. By conducting experiments with three different PPI corpuses, we show that PPISpotter is superior to the other techniques incorporated into semi-supervised SVMs such as Random Sampling, Clustering, and Transductive SVMs by precision, recall, and F-measure. Our system is a novel, state-of-the-art technique for efficiently extracting protein-protein interaction pairs.
Bibliographical noteFunding Information:
Partial support for this research was provided by the National Science Foundation under grant DUE-0937629 and by the New Jersey Institute of Technology. This work was also supported by the Brain Korea 21 Project in 2010 and Mid-career Researcher Program through NRF grant funded by the MEST (No. KRF-2009-0080667). This article has been published as part of BMC Bioinformatics Volume 12 Supplement 12, 2011: Selected articles from the 9th International Workshop on Data Mining in Bioinformatics (BIOKDD). The full contents of the supplement are available online at http://www.biomedcentral.com/ bmcbioinformatics/12?issue=S12.
All Science Journal Classification (ASJC) codes
- Structural Biology
- Molecular Biology
- Computer Science Applications
- Applied Mathematics