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Genome Biol. 2008;9 Suppl 2:S13. doi: 10.1186/gb-2008-9-s2-s13. Epub 2008 Sep 1.

OntoGene in BioCreative II.

Author information

  • 1Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland. rinaldi@cl.uzh.ch

Abstract

BACKGROUND:

Research scientists and companies working in the domains of biomedicine and genomics are increasingly faced with the problem of efficiently locating, within the vast body of published scientific findings, the critical pieces of information that are needed to direct current and future research investment.

RESULTS:

In this report we describe approaches taken within the scope of the second BioCreative competition in order to solve two aspects of this problem: detection of novel protein interactions reported in scientific articles, and detection of the experimental method that was used to confirm the interaction. Our approach to the former problem is based on a high-recall protein annotation step, followed by two strict disambiguation steps. The remaining proteins are then combined according to a number of lexico-syntactic filters, which deliver high-precision results while maintaining reasonable recall. The detection of the experimental methods is tackled by a pattern matching approach, which has delivered the best results in the official BioCreative evaluation.

CONCLUSION:

Although the results of BioCreative clearly show that no tool is sufficiently reliable for fully automated annotations, a few of the proposed approaches (including our own) already perform at a competitive level. This makes them interesting either as standalone tools for preliminary document inspection, or as modules within an environment aimed at supporting the process of curation of biomedical literature.

PMID:
18834491
[PubMed - indexed for MEDLINE]
PMCID:
PMC2559984
Free PMC Article

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