Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 7, Issue 3, Pages 385-399Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2010.61
Keywords
Text mining; text analysis; natural language processing; molecular biology; biological curation
Categories
Funding
- US National Science Foundation (NSF) [IIS-0844419]
- European Commission [LSHG-CT-2005-518254, LSG-CT-2004-512092]
- Spanish National Bioinformatics Institute
- FEBS
- AIRC
- Telethon
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [0844419] Funding Source: National Science Foundation
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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.
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