Combining active learning and semi-supervised learning techniques to extract protein interaction sentences View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2011-12

AUTHORS

Min Song, Hwanjo Yu, Wook-Shin Han

ABSTRACT

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: Our system is a novel, state-of-the-art technique for efficiently extracting protein-protein interaction pairs. More... »

PAGES

s4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-12-s12-s4

DOI

http://dx.doi.org/10.1186/1471-2105-12-s12-s4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1033337271

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/22168401


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