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

He, Yulan and Chenghua, Lin (2009). Protein-protein interactions classification from text via local learning with class priors. In: 14th International Conference on Applications of Natural Language to Information Systems, June 23-26, 2009, Saarbrücken, Germany, pp. 182–191.

URL: http://www.springerlink.com/content/978-3-642-1254...
DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1007/978-3-642-12550-8_15
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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 semi-supervised learning algorithms such as SVM and it also performs better than local learning without incorporating class priors.

Item Type: Conference Item
Copyright Holders: 2009 Springer-Verlag
ISSN: 0302-9743
Extra Information: Natural 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

Helmut Horacek, Elisabeth Métais, Rafael Muñoz and Magdalena Wolska

ISBN 978-3-642-12549-2

Lecture Notes in Computer Science Volume 5723, 2010
Academic Unit/Department: Knowledge Media Institute
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 24607
Depositing User: Yulan He
Date Deposited: 07 Dec 2010 09:07
Last Modified: 25 Jun 2014 21:52
URI: http://oro.open.ac.uk/id/eprint/24607
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