An Overview of BioCreative II.5

作者：Leitner Florian^*; Mardis Scott A; Krallinger Martin; Cesareni Gianni; Hirschman Lynette A; Valencia Alfonso

来源：IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2010, 7(3): 385-399.

DOI：10.1109/TCBB.2010.61

• Text mining
• text analysis
• natural language processing
• molecular biology
• biological curation

We present the results of the BioCreative II.5 evaluation in association with the FEBS Letters experiment, where authors created Structured Digital Abstracts to capture information about protein-protein interactions. The BioCreative II.5 challenge evaluated automatic annotations from 15 text mining teams based on a gold standard created by reconciling annotations from curators, authors, and automated systems. The tasks were to rank articles for curation based on curatable protein-protein interactions; to identify the interacting proteins (using UniProt identifiers) in the positive articles (61); and to identify interacting protein pairs. There were 595 full-text articles in the evaluation test set, including those both with and without curatable protein interactions. The principal evaluation metrics were the interpolated area under the precision/recall curve (AUC iP/R), and ( balanced) F-measure. For article classification, the best AUC iP/R was 0.70; for interacting proteins, the best system achieved good macroaveraged recall (0.73) and interpolated area under the precision/recall curve (0.58), after filtering incorrect species and mapping homonymous orthologs; for interacting protein pairs, the top (filtered, mapped) recall was 0.42 and AUC iP/R was 0.29. Ensemble systems improved performance for the interacting protein task.

• 出版日期2010-9