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 |
Journal | BMC bioinformatics |
Volume | 9 Suppl 3 |
Pagination | S9 |
Date Published | 2008 |
ISSN | 1471-2105 |
Keywords | Algorithms, Artificial Intelligence, Genes, MEDLINE, Natural Language Processing, Pattern Recognition, Automated, Proteins, Sensitivity and Specificity, Terminology as Topic, Vocabulary, Controlled |
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. |
DOI | 10.1186/1471-2105-9-S3-S9 |
Alternate Journal | BMC Bioinformatics |
PubMed ID | 18426554 |