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Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction |
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Published in | BMC bioinformatics. 2008, vol. 9 Suppl 3, S9 | |
Abstract | BACKGROUND: This paper describes and evaluates a sentence selection engine that extracts a GeneRiF (Gene Reference into Functions) as defined in ENTREZ-Gene based on a MEDLINE record. Inputs for this task include both a gene and a pointer to a MEDLINE reference. In the suggested approach we merge two independent sentence extraction strategies. The first proposed strategy (LASt) uses argumentative features, inspired by discourse-analysis models. The second extraction scheme (GOEx) uses an automatic text categorizer to estimate the density of Gene Ontology categories in every sentence; thus providing a full ranking of all possible candidate GeneRiFs. A combination of the two approaches is proposed, which also aims at reducing the size of the selected segment by filtering out non-content bearing rhetorical phrases. RESULTS: Based on the TREC-2003 Genomics collection for GeneRiF identification, the LASt extraction strategy is already competitive (52.78%). When used in a combined approach, the extraction task clearly shows improvement, achieving a Dice score of over 57% (+10%). CONCLUSIONS: Argumentative representation levels and conceptual density estimation using Gene Ontology contents appear complementary for functional annotation in proteomics. | |
Keywords | Algorithms — Artificial Intelligence — Genes/genetics — MEDLINE — Natural Language Processing — Pattern Recognition, Automated/methods — Proteins/classification/genetics — Sensitivity and Specificity — Terminology as Topic — Vocabulary, Controlled | |
Identifiers | PMID: 18426554 | |
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Research group | Swiss-Prot Research Group | |
Citation (ISO format) | GOBEILL, Julien et al. Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction. In: BMC bioinformatics, 2008, vol. 9 Suppl 3, p. S9. doi: 10.1186/1471-2105-9-S3-S9 https://archive-ouverte.unige.ch/unige:1065 |