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Presentation
PathBinder – text empirics and automatic extraction of biomolecular interactions
BMC Bioinformatics
  • Lifeng Zhang, Iowa State University
  • Daniel Berleant, University of Arkansas at Little Rock
  • Jing Ding, Ohio State University Medical Center
  • Tuan Cao, Iowa State University
  • Eve S. Wurtele, Iowa State University
Document Type
Conference Proceeding
Conference
Sixth Annual MCBIOS Conference
Publication Version
Published Version
Publication Date
1-1-2009
DOI
10.1186/1471-2105-10-S11-S18
Conference Title
Transformational Bioinformatics: Delivering Value from Genomes
Conference Date
February 20-21, 2009
Geolocation
(33.4503998, -88.81838719999996)
Abstract
Motivation The increasingly large amount of free, online biological text makes automatic interaction extraction correspondingly attractive. Machine learning is one strategy that works by uncovering and using useful properties that are implicit in the text. However these properties are usually not reported in the literature explicitly. By investigating specific properties of biological text passages in this paper, we aim to facilitate an alternative strategy, the use of text empirics, to support mining of biomedical texts for biomolecular interactions. We report on our application of this approach, and also report some empirical findings about an important class of passages. These may be useful to others who may also wish to use the empirical properties we describe. Results We manually analyzed syntactic and semantic properties of sentences likely to describe interactions between biomolecules. The resulting empirical data were used to design an algorithm for the PathBinder system to extract biomolecular interactions from texts. PathBinder searches PubMed for sentences describing interactions between two given biomolecules. PathBinder then uses probabilistic methods to combine evidence from multiple relevant sentences in PubMed to assess the relative likelihood of interaction between two arbitrary biomolecules. A biomolecular interaction network was constructed based on those likelihoods. Conclusion The text empirics approach used here supports computationally friendly, performance competitive, automatic extraction of biomolecular interactions from texts.
Comments

This is a proceeding from Sixth Annual MCBIOS Conference. Transformational Bioinformatics: Delivering Value from Genomes 10 (2009): S18, doi: 10.1186/1471-2105-10-S11-S18. Posted with permission.

Rights
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright Owner
Zhang et al
Language
en
File Format
application/pdf
Citation Information
Lifeng Zhang, Daniel Berleant, Jing Ding, Tuan Cao, et al.. "PathBinder – text empirics and automatic extraction of biomolecular interactions" Starkville, MS, USABMC Bioinformatics Vol. 10 Iss. Supplement 11 (2009) p. S18
Available at: http://works.bepress.com/eve-wurtele/46/