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Title

Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction
Authors
Gobeill, Julien
Tbahriti, Imad
Ehrler, Frédéric
Mottaz, Anais
Veuthey, Anne-Lise
Ruch, Patrick
Published in BMC Bioinformatics. 2008, vol. 9 Suppl 3, p. 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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Article (Accepted version) (300 Kb) - public document Free access
Other version: http://www.ncbi.nlm.nih.gov/sites/entrez
Structures
Faculté des sciences / Département d'informatique
Centres et instituts / Centre universitaire d'informatique
Faculté de médecine / Section de médecine clinique / Département de radiologie et informatique médicale
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

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Deposited on : 2009-03-04

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