Open AccessUnsupervised gene function extraction using semantic vectors
Author(s) -
Ehsan Emadzadeh,
Azadeh Nikfarjam,
Rachel Ginn,
Graciela GonzalezHernandez
Publication year - 2014
Publication title -
database
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.406
H-Index - 62
ISSN - 1758-0463
DOI - 10.1093/database/bau084
Subject(s) - computer science , task (project management) , semantic similarity , set (abstract data type) , ontology , function (biology) , information retrieval , similarity (geometry) , natural language processing , measure (data warehouse) , information extraction , artificial intelligence , similarity measure , training set , test set , data mining , image (mathematics) , biology , programming language , philosophy , management , epistemology , evolutionary biology , economics
Finding gene functions discussed in the literature is an important task of information extraction (IE) from biomedical documents. Automated computational methodologies can significantly reduce the need for manual curation and improve quality of other related IE systems. We propose an open-IE method for the BioCreative IV GO shared task (subtask b), focused on finding gene function terms [Gene Ontology (GO) terms] for different genes in an article. The proposed open-IE approach is based on distributional semantic similarity over the GO terms. The method does not require annotated data for training, which makes it highly generalizable. We achieve an F-measure of 0.26 on the test-set in the official submission for BioCreative-GO shared task, the third highest F-measure among the seven participants in the shared task.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom