|Title||Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction.|
|Publication Type||Journal Article|
|Year of Publication||2008|
|Authors||Gobeill, J, Tbahriti, I, Ehrler, F, Mottaz, A, Veuthey, A-L, Ruch, P|
|Volume||9 Suppl 3|
|Keywords||Algorithms, Artificial Intelligence, Genes, MEDLINE, Natural Language Processing, Pattern Recognition, Automated, Proteins, Sensitivity and Specificity, Terminology as Topic, Vocabulary, Controlled|
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.
|Alternate Journal||BMC Bioinformatics|