Nucleic Acids Research Advance Access published online on May 16, 2008
Nucleic Acids Research, doi:10.1093/nar/gkn296
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Web Server Issue |
PolySearch: a web-based text mining system for extracting relationships between human diseases, genes, mutations, drugs and metabolites
1Department of Computing Science, University of Alberta, Canada T6G 2E8, 2Department of Biological Sciences, University of Alberta, Canada T6G 2E6, 3Department of Laboratory Medicine, University of Alberta, Canada T6G 1E5 and 4National Research Council, National Institute for Nanotechnology (NINT), Edmonton, AB, Canada T6G 2M9
*To whom correspondence should be addressed. Tel: +780 492 0383; Fax: +780 492 5305; Email: david.wishart{at}ualberta.ca
Received February 1, 2008. Revised April 11, 2008. Accepted April 29, 2008.
A particular challenge in biomedical text mining is to find ways of handling ‘comprehensive’ or ‘associative’ queries such as ‘Find all genes associated with breast cancer’. Given that many queries in genomics, proteomics or metabolomics involve these kind of comprehensive searches we believe that a web-based tool that could support these searches would be quite useful. In response to this need, we have developed the PolySearch web server. PolySearch supports >50 different classes of queries against nearly a dozen different types of text, scientific abstract or bioinformatic databases. The typical query supported by PolySearch is ‘Given X, find all Y's’ where X or Y can be diseases, tissues, cell compartments, gene/protein names, SNPs, mutations, drugs and metabolites. PolySearch also exploits a variety of techniques in text mining and information retrieval to identify, highlight and rank informative abstracts, paragraphs or sentences. PolySearch's performance has been assessed in tasks such as gene synonym identification, protein–protein interaction identification and disease gene identification using a variety of manually assembled ‘gold standard’ text corpuses. Its f-measure on these tasks is 88, 81 and 79%, respectively. These values are between 5 and 50% better than other published tools. The server is freely available at http://wishart.biology.ualberta.ca/polysearch
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  E. Lim, A. Pon, Y. Djoumbou, C. Knox, S. Shrivastava, A. C. Guo, V. Neveu, and D. S. Wishart T3DB: a comprehensively annotated database of common toxins and their targets Nucleic Acids Res., November 6, 2009; (2009) gkp934v1. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Frisch, B. Klocke, M. Haltmeier, and K. Frech LitInspector: literature and signal transduction pathway mining in PubMed abstracts Nucleic Acids Res., July 1, 2009; 37(suppl_2): W135 - W140. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. Plake, L. Royer, R. Winnenburg, J. Hakenberg, and M. Schroeder GoGene: gene annotation in the fast lane Nucleic Acids Res., July 1, 2009; 37(suppl_2): W300 - W304. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 I. Spasic, E. Simeonidis, H. L. Messiha, N. W. Paton, and D. B. Kell KiPar, a tool for systematic information retrieval regarding parameters for kinetic modelling of yeast metabolic pathways Bioinformatics, June 1, 2009; 25(11): 1404 - 1411. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. C. Tsoi, M. Boehnke, R. L. Klein, and W. J. Zheng Evaluation of genome-wide association study results through development of ontology fingerprints Bioinformatics, May 15, 2009; 25(10): 1314 - 1320. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Hur, A. D. Schuyler, D. J. States, and E. L. Feldman SciMiner: web-based literature mining tool for target identification and functional enrichment analysis Bioinformatics, March 15, 2009; 25(6): 838 - 840. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Tsuruoka, J. Tsujii, and S. Ananiadou FACTA: a text search engine for finding associated biomedical concepts Bioinformatics, November 1, 2008; 24(21): 2559 - 2560. [Abstract] [Full Text] [PDF]
|
|