Protein-Protein Interactions Classification from Text via Local Learning with Class Priors

Yulan He, Chenghua Lin

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

1 Citation (Scopus)


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 semi-supervised learning algorithms such as SVM and it also performs better than local learning without incorporating class priors.
Original languageEnglish
Title of host publicationNatural Language Processing and Information Systems
Subtitle of host publication14th International Conference on Applications of Natural Language to Information Systems, NLDB 2009, Saarbrücken, Germany, June 24-26, 2009. Revised Papers
PublisherSpringer Berlin / Heidelberg
Number of pages10
ISBN (Electronic)978-3-642-12550-8
ISBN (Print)978-3-642-12549-2
Publication statusPublished - 2010

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743


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