Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction

Julien Gobeill, Imad Tbahriti, Frédéric Ehrler, Anaïs Mottaz, Anne-Lise Veuthey, Patrick Ruch
2008 BMC Bioinformatics  
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
more » ... gorizer 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.
doi:10.1186/1471-2105-9-s3-s9 pmid:18426554 pmcid:PMC2352866 fatcat:gso7ix72qjexfkoey62n6wkrzq