Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction.
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.
Gobeill
Julien
J
University and Hospitals of Geneva, Geneva, Switzerland.
Tbahriti
Imad
I
Ehrler
Frédéric
F
Mottaz
Anaïs
A
Veuthey
Anne-Lise
AL
Ruch
Patrick
P
D000465:Algorithms; D001185:Artificial Intelligence; D005796:Genes / Q000235:genetics*; D016239:MEDLINE; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D011506:Proteins / Q000145:classification* / Q000235:genetics*; D012680:Sensitivity and Specificity; D009626:Terminology as Topic; D018875:Vocabulary, Controlled
D011506:Proteins