Nucleic Acids Research Advance Access originally published online on May 28, 2008
Nucleic Acids Research 2008 36(Web Server issue):W411-W415; doi:10.1093/nar/gkn281
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Nucleic Acids Research, 2008, Vol. 36, No. suppl_2 W411-W415
© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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PIE: an online prediction system for protein–protein interactions from text
1Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University, Seoul 151-744, Korea, 2Manufacturing Systems Integration Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA and 3Center for Bioinformation Technology, Graduate Program in Bioinformatics, Seoul National University, Seoul 151-742, Korea
*To whom correspondence should be addressed. Tel: +82 2 880 1847; Fax: +82 2 875 2240; Email: btzhang{at}bi.snu.ac.kr
Received February 7, 2008. Revised April 16, 2008. Accepted April 26, 2008.
Protein–protein interaction (PPI) extraction has been an important research topic in bio-text mining area, since the PPI information is critical for understanding biological processes. However, there are very few open systems available on the Web and most of the systems focus on keyword searching based on predefined PPIs. PIE (Protein Interaction information Extraction system) is a configurable Web service to extract PPIs from literature, including user-provided papers as well as PubMed articles. After providing abstracts or papers, the prediction results are displayed in an easily readable form with essential, yet compact features. The PIE interface supports more features such as PDF file extraction, PubMed search tool and network communication, which are useful for biologists and bio-system developers. The PIE system utilizes natural language processing techniques and machine learning methodologies to predict PPI sentences, which results in high precision performance for Web users. PIE is freely available at http://bi.snu.ac.kr/pie/.
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors
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