Abstract

Background The goal of the Gene Normalization task is to link genes or gene products mentioned in the literature to biological databases a key step for accurate search of the biological literature. This 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 gene or gene products mentioned in 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 (785 identifiers), with a gold standard created by expert annotators. Interannotator 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 0.80-0.81. 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 BioCreative II include broader participation (20 vs. 8 teams) and a pooled system performance comparable to human experts--over 90% agreement. These results show promise as tools to link the literature with biological databases.



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