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Bioinformatics 2006 22(14):e220-e226; doi:10.1093/bioinformatics/btl203
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Finding the evidence for protein-protein interactions from PubMed abstracts

Hyunchul Jang 1,*, Jaesoo Lim 1, Joon-Ho Lim 1, Soo-Jun Park 1, Kyu-Chul Lee 2 and Seon-Hee Park 1

1 Bioinformatics Team, Electronics and Telecommunications Research Institute (ETRI) Gajeong-Dong, Yuseong-Gu, Daejeon, 305-350, Korea
2 Department of Computer Engineering, Chungnam National University Gung-Dong, Yuseong-Gu, Daejeon, 305-764, Korea

*To whom correspondence should be addressed.

Motivation: Protein-protein interactions play critical roles in biological processes, and many biologists try to find or to predict crucial information concerning these interactions. Before verifying interactions in biological laboratory work, validating them from previous research is necessary. Although many efforts have been made to create databases that store verified information in a structured form, much interaction information still remains as unstructured text. As the amount of new publications has increased rapidly, a large amount of research has sought to extract interactions from the text automatically. However, there remain various difficulties associated with the process of applying automatically generated results into manually annotated databases. For interactions that are not found in manually stored databases, researchers attempt to search for abstracts or full papers.

Results: As a result of a search for two proteins, PubMed frequently returns hundreds of abstracts. In this paper, a method is introduced that validates protein-protein interactions from PubMed abstracts. A query is generated from two given proteins automatically and abstracts are then collected from PubMed. Following this, target proteins and their synonyms are recognized and their interaction information is extracted from the collection. It was found that 67.37% of the interactions from DIP-PPI corpus were found from the PubMed abstracts and 87.37% of interactions were found from the given full texts.

Availability: Contact authors.

Contact: janghc{at}etri.re.kr


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 Nucleic Acids ResHome page
S. Kim, S.-Y. Shin, I.-H. Lee, S.-J. Kim, R. Sriram, and B.-T. Zhang
PIE: an online prediction system for protein-protein interactions from text
Nucleic Acids Res., July 1, 2008; 36(suppl_2): W411 - W415.
[Abstract] [Full Text] [PDF]


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