Protein-protein interactions classification from text via local learning with class priors

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Abstract

Text classification is essential for narrowing down the number of documents relevant to a particular topic for further pursual, especially when searching through large biomedical databases. Protein-protein interactions are an example of such a topic with databases being devoted specifically to them. This paper proposed a semi-supervised learning algorithm via local learning with class priors (LL-CP) for biomedical text classification where unlabeled data points are classified in a vector space based on their proximity to labeled nodes. The algorithm has been evaluated on a corpus of biomedical documents to identify abstracts containing information about protein-protein interactions with promising results. Experimental results show that LL-CP outperforms the traditional semisupervised learning algorithms such as SVMand it also performs better than local learning without incorporating class priors.

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Publication date2009
Publication titleNatural language processing and information systems : 14th international conference on applications of natural language to information systems, NLDB 2009, Saarbrücken, Germany, June 24-26, 2009. Revised Papers
EditorsHelmut Horacek, Elisabeth Métais, Rafael Muñoz, Magdalena Wolska
PublisherSpringer
Pages182-191
Number of pages10
Volume5723
ISBN (Print)3-642-12549-2, 978-3-642-12549-2
Original languageEnglish
Event14th international conference on applications of natural language to information systems, NLDB 2009 - Saarbrücken, Germany

Publication series

NameLecture notes in computer science
PublisherSpringer
Volume5723
ISSN (Print)0302-9743

Conference

Conference14th international conference on applications of natural language to information systems, NLDB 2009
CountryGermany
CitySaarbrücken
Period24/06/0926/06/09

Keywords

  • text classification, protein-protein interactions extraction, semi-supervised learning, local learning

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