Combining active learning and semi-supervised learning techniques to extract protein interaction sentences.

Document Type

Article

Publication Date

1-1-2011

Abstract

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.

Identifier

84864998804 (Scopus)

Publication Title

BMC Bioinformatics

External Full Text Location

https://doi.org/10.1186/1471-2105-12-S12-S4

e-ISSN

14712105

PubMed ID

22168401

First Page

S4

Volume

12 Suppl 12

Grant

DUE-0937629

Fund Ref

National Research Foundation of Korea

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