Skip Navigation


Bioinformatics Advance Access originally published online on July 26, 2005
Bioinformatics 2006 22(6):645-650; doi:10.1093/bioinformatics/bti597
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
22/6/645    most recent
bti597v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (6)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Saric, J.
Right arrow Articles by Bork, P.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Saric, J.
Right arrow Articles by Bork, P.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Extraction of regulatory gene/protein networks from Medline

Jasmin Saric 1,*,{dagger}, Lars Juhl Jensen 2,{dagger}, Rossitza Ouzounova 2, Isabel Rojas 1 and Peer Bork 2

1EML Research gGmbH D-69118 Heidelberg, Germany
2European Molecular Biology Laboratory D-69117 Heidelberg, Germany

*To whom correspondence should be addressed.

ABSTRACT

Motivation: We have previously developed a rule-based approach for extracting information on the regulation of gene expression in yeast. The biomedical literature, however, contains information on several other equally important regulatory mechanisms, in particular phosphorylation, which we now expanded for our rule-based system also to extract.

Results: This paper presents new results for extraction of relational information from biomedical text. We have improved our system, STRING-IE, to capture both new types of linguistic constructs as well as new types of biological information [i.e. (de-)phosphorylation]. The precision remains stable with a slight increase in recall. From almost one million PubMed abstracts related to four model organisms, we manage to extract regulatory networks and binary phosphorylations comprising 3319 relation chunks. The accuracy is 83–90% and 86–95% for gene expression and (de-)phosphorylation relations, respectively. To achieve this, we made use of an organism-specific resource of gene/protein names considerably larger than those used in most other biology related information extraction approaches. These names were included in the lexicon when retraining the part-of-speech (POS) tagger on the GENIA corpus. For the domain in question, an accuracy of 96.4% was attained on POS tags. It should be noted that the rules were developed for yeast and successfully applied to both abstracts and full-text articles related to other organisms with comparable accuracy.

Availability: The revised GENIA corpus, the POS tagger, the extraction rules and the full sets of extracted relations are available from http://www.bork.embl.de/Docu/STRING-IE

Contact: saric{at}eml-r.org


Received on April 15, 2005; revised on June 20, 2005; accepted on July 22, 2005

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
Nucleic Acids ResHome page
M. Kuhn, C. von Mering, M. Campillos, L. J. Jensen, and P. Bork
STITCH: interaction networks of chemicals and proteins
Nucleic Acids Res., January 11, 2008; 36(suppl_1): D684 - D688.
[Abstract] [Full Text] [PDF]


Home page
BioinformaticsHome page
J.-H. Kim, A. Mitchell, T. K. Attwood, and M. Hilario
Learning to extract relations for protein annotation
Bioinformatics, July 1, 2007; 23(13): i256 - i263.
[Abstract] [Full Text] [PDF]


Home page
BioinformaticsHome page
K. Fundel, R. Kuffner, and R. Zimmer
RelEx--Relation extraction using dependency parse trees
Bioinformatics, February 1, 2007; 23(3): 365 - 371.
[Abstract] [Full Text] [PDF]



Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.