Discovery of protein-protein interactions using a combination of linguistic, statistical and graphical information

<p>Abstract</p> <p>Background</p> <p>The rapid publication of important research in the biomedical literature makes it increasingly difficult for researchers to keep current with significant work in their area of interest.</p> <p>Results</p> <p>T...

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Main Authors: Kershenbaum Aaron, Cooper James W
Format: Article
Language:English
Published: BMC 2005-06-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/6/143
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spelling doaj-32e09bd862a54c27a27b0cc2b9fdab3e2020-11-25T01:56:59ZengBMCBMC Bioinformatics1471-21052005-06-016114310.1186/1471-2105-6-143Discovery of protein-protein interactions using a combination of linguistic, statistical and graphical informationKershenbaum AaronCooper James W<p>Abstract</p> <p>Background</p> <p>The rapid publication of important research in the biomedical literature makes it increasingly difficult for researchers to keep current with significant work in their area of interest.</p> <p>Results</p> <p>This paper reports a scalable method for the discovery of protein-protein interactions in Medline abstracts, using a combination of text analytics, statistical and graphical analysis, and a set of easily implemented rules. Applying these techniques to 12,300 abstracts, a precision of 0.61 and a recall of 0.97 were obtained, (f = 0.74) and when allowing for two-hop and three-hop relations discovered by graphical analysis, the precision was 0.74 (f = 0.83).</p> <p>Conclusion</p> <p>This combination of linguistic and statistical approaches appears to provide the highest precision and recall thus far reported in detecting protein-protein relations using text analytic approaches.</p> http://www.biomedcentral.com/1471-2105/6/143
collection DOAJ
language English
format Article
sources DOAJ
author Kershenbaum Aaron
Cooper James W
spellingShingle Kershenbaum Aaron
Cooper James W
Discovery of protein-protein interactions using a combination of linguistic, statistical and graphical information
BMC Bioinformatics
author_facet Kershenbaum Aaron
Cooper James W
author_sort Kershenbaum Aaron
title Discovery of protein-protein interactions using a combination of linguistic, statistical and graphical information
title_short Discovery of protein-protein interactions using a combination of linguistic, statistical and graphical information
title_full Discovery of protein-protein interactions using a combination of linguistic, statistical and graphical information
title_fullStr Discovery of protein-protein interactions using a combination of linguistic, statistical and graphical information
title_full_unstemmed Discovery of protein-protein interactions using a combination of linguistic, statistical and graphical information
title_sort discovery of protein-protein interactions using a combination of linguistic, statistical and graphical information
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2005-06-01
description <p>Abstract</p> <p>Background</p> <p>The rapid publication of important research in the biomedical literature makes it increasingly difficult for researchers to keep current with significant work in their area of interest.</p> <p>Results</p> <p>This paper reports a scalable method for the discovery of protein-protein interactions in Medline abstracts, using a combination of text analytics, statistical and graphical analysis, and a set of easily implemented rules. Applying these techniques to 12,300 abstracts, a precision of 0.61 and a recall of 0.97 were obtained, (f = 0.74) and when allowing for two-hop and three-hop relations discovered by graphical analysis, the precision was 0.74 (f = 0.83).</p> <p>Conclusion</p> <p>This combination of linguistic and statistical approaches appears to provide the highest precision and recall thus far reported in detecting protein-protein relations using text analytic approaches.</p>
url http://www.biomedcentral.com/1471-2105/6/143
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AT cooperjamesw discoveryofproteinproteininteractionsusingacombinationoflinguisticstatisticalandgraphicalinformation
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