Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2008-04

AUTHORS

Julien Gobeill, Imad Tbahriti, Frédéric Ehrler, Anaïs Mottaz, Anne-Lise Veuthey, Patrick Ruch

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. More... »

PAGES

s9

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-9-s3-s9

DOI

http://dx.doi.org/10.1186/1471-2105-9-s3-s9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1016498582

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/18426554


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