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Validating text mining results on protein-protein interactions using gene expression profiles

Zhou, Deyu; He, Yulan and Kwoh, Chee Keong (2006). Validating text mining results on protein-protein interactions using gene expression profiles. In: Proceedings of the 2006 International Conference on Biomedical & Pharmaceutical Engineering, Research Publishing Services, Singapore, pp. 580–585.

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Protein-protein interactions referring to the associations of protein molecules are crucial for many biological functions. Since most knowledge about them still hides in biological publications, there is an increasing focus on mining information from the vast amount of biological literature such as MedLine. Many approaches, such as pattern matching, shallow parsing and deep parsing, have been proposed to automatically extract protein-protein interaction information from text sources, with however limited success. Moreover, to the best of our knowledge, none of the existing approaches have performed automatic validation on the mining results. In this paper, we describe a novel framework in which text mining results are automatically validated using the knowledge mined from gene expression profiles. A probability model is proposed to score the confidence of protein-protein interactions based on both text mining results and gene expression profiles. Experimental results are presented to show the feasibility of this framework.

Item Type: Conference or Workshop Item
Copyright Holders: 2006 Research Publishing Services
ISBN: 81-904262-4-9, 978-81-904262-4-4
Extra Information: ISBN: 9789810579432
INSPEC Accession Number: 9751568
Keywords: medLine; automatic validation; biological functions; biological literature; confidence score; deep parsing; gene expression profiles; pattern matching; probability model; protein-protein interactions; shallow parsing; text mining; text sources
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 23799
Depositing User: Kay Dave
Date Deposited: 29 Mar 2011 13:16
Last Modified: 15 Dec 2018 06:36
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