Overview of BioCreative II gene normalization

Alexander A. Morgan(Stanford University), Zhiyong Lu(University of Colorado Denver), Xinglong Wang(University of Edinburgh), Aaron Cohen(Oregon Health & Science University), Juliane Fluck(Fraunhofer Institute for Algorithms and Scientific Computing), Patrick Ruch(University Hospital of Geneva), Anna Divoli(University of California, Berkeley), Katrin Fundel(Ludwig-Maximilians-Universität München), Robert Leaman(Arizona State University), Jörg Hakenberg(Technische Universität Dresden), Chengjie Sun(Harbin Institute of Technology), Heng-hui Liu(Georgetown University), Rafael Torres, Michael Krauthammer(Yale University), William W. Lau(National Institutes of Health), Hongfang Liu(Georgetown University), Chun-Nan Hsu(Institute of Information Science, Academia Sinica), Martijn J. Schuemie(Erasmus MC), Kevin Bretonnel Cohen(Mitre (United States)), Lynette Hirschman(Mitre (United States))
Genome biology
September 1, 2008
Cited by 329Open Access
Full Text

Abstract

BACKGROUND: The goal of the gene normalization task is to link genes or gene products mentioned in the literature to biological databases. This is a key step in an accurate search of the biological literature. It is a challenging task, even for the human expert; genes are often described rather than referred to by gene symbol and, confusingly, one gene name may refer to different genes (often from different organisms). For BioCreative II, the task was to list the Entrez Gene identifiers for human genes or gene products mentioned in PubMed/MEDLINE abstracts. We selected abstracts associated with articles previously curated for human genes. We provided 281 expert-annotated abstracts containing 684 gene identifiers for training, and a blind test set of 262 documents containing 785 identifiers, with a gold standard created by expert annotators. Inter-annotator agreement was measured at over 90%. RESULTS: Twenty groups submitted one to three runs each, for a total of 54 runs. Three systems achieved F-measures (balanced precision and recall) between 0.80 and 0.81. Combining the system outputs using simple voting schemes and classifiers obtained improved results; the best composite system achieved an F-measure of 0.92 with 10-fold cross-validation. A 'maximum recall' system based on the pooled responses of all participants gave a recall of 0.97 (with precision 0.23), identifying 763 out of 785 identifiers. CONCLUSION: Major advances for the BioCreative II gene normalization task include broader participation (20 versus 8 teams) and a pooled system performance comparable to human experts, at over 90% agreement. These results show promise as tools to link the literature with biological databases.


Related Papers

No related papers found

Powered by citation graph analysis