This article is part of the supplement: Selected articles from the 9th International Workshop on Data Mining in Bioinformatics (BIOKDD)
Combining active learning and semi-supervised learning techniques to extract protein interaction sentences
- Equal contributors
1 Information Systems Department, New Jersey Institute of Technology, University Heights, Newark, New Jersey, USA
2 Department of Computer Science & Engineering, POSTECH, Pohang, South Korea
3 School of IT Engineering, Kyungpook National University, Daegu, South Korea
Citation and License
BMC Bioinformatics 2011, 12(Suppl 12):S4 doi:10.1186/1471-2105-12-S12-S4Published: 24 November 2011
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.