Zhou, Deyu; He, Yulan and Kwoh, Chee Keong
(2006).
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| URL: | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumb... |
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Abstract
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 Item |
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| Copyright Holders: | 2006 Research Publishing Services |
| 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/Department: | Knowledge Media Institute |
| Interdisciplinary Research Centre: | Centre for Research in Computing (CRC) |
| Item ID: | 23799 |
| Depositing User: | Kay Dave |
| Date Deposited: | 29 Mar 2011 13:16 |
| Last Modified: | 28 Oct 2012 07:46 |
| URI: | http://oro.open.ac.uk/id/eprint/23799 |
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