This article is part of the supplement: The Third BioCreative - Critical Assessment of Information Extraction in Biology Challenge

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The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text

Martin Krallinger1*, Miguel Vazquez1, Florian Leitner1, David Salgado2, Andrew Chatr-aryamontri3, Andrew Winter3, Livia Perfetto4, Leonardo Briganti4, Luana Licata4, Marta Iannuccelli4, Luisa Castagnoli4, Gianni Cesareni45, Mike Tyers3, Gerold Schneider6, Fabio Rinaldi6, Robert Leaman7, Graciela Gonzalez8, Sergio Matos9, Sun Kim10, W John Wilbur10, Luis Rocha11, Hagit Shatkay12, Ashish V Tendulkar13, Shashank Agarwal14, Feifan Liu14, Xinglong Wang15, Rafal Rak15, Keith Noto16, Charles Elkan17, Zhiyong Lu10, Rezarta Islamaj Dogan10, Jean-Fred Fontaine18, Miguel A Andrade-Navarro18 and Alfonso Valencia1

Author Affiliations

1 Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain

2 Australian Regenerative Medicine Institute, Monash University, Australia

3 School of Biological Sciences, University of Edinburgh, Edinburgh, UK

4 Department of Biology, University of Rome Tor Vergata, Rome, Italy

5 IRCSS, Fondazione Santa Lucia, Rome, Italy

6 Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland

7 School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, USA

8 Department of Biomedical Informatics, Arizona State University, Tempe, Arizona, USA

9 Institute of Electronics and Telematics Engineering of Aveiro, University of Aveiro Campus Universitario de Santiago, 3810-193 Aveiro, Portugal

10 National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, Maryland, 20894, USA

11 School of Informatics and Computing, Indiana University, 919 E. 10th St Bloomington IN, 47408, USA

12 Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA

13 Department of Computer Science and Engineering, IIT Madras, Chennai-600 036, India

14 Medical Informatics, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA

15 National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester, UK

16 Department of Computer Science, Tufts University, 161 College Ave, Medford, MA 02155, USA

17 Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA

18 Computational Biology and Data Mining Group, Max-Delbrück-Centrum für Molekulare Medizin, Robert-Rössle-Str. 10, 13125 Berlin, Germany

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BMC Bioinformatics 2011, 12(Suppl 8):S3  doi:10.1186/1471-2105-12-S8-S3

Published: 3 October 2011



Determining usefulness of biomedical text mining systems requires realistic task definition and data selection criteria without artificial constraints, measuring performance aspects that go beyond traditional metrics. The BioCreative III Protein-Protein Interaction (PPI) tasks were motivated by such considerations, trying to address aspects including how the end user would oversee the generated output, for instance by providing ranked results, textual evidence for human interpretation or measuring time savings by using automated systems. Detecting articles describing complex biological events like PPIs was addressed in the Article Classification Task (ACT), where participants were asked to implement tools for detecting PPI-describing abstracts. Therefore the BCIII-ACT corpus was provided, which includes a training, development and test set of over 12,000 PPI relevant and non-relevant PubMed abstracts labeled manually by domain experts and recording also the human classification times. The Interaction Method Task (IMT) went beyond abstracts and required mining for associations between more than 3,500 full text articles and interaction detection method ontology concepts that had been applied to detect the PPIs reported in them.


A total of 11 teams participated in at least one of the two PPI tasks (10 in ACT and 8 in the IMT) and a total of 62 persons were involved either as participants or in preparing data sets/evaluating these tasks. Per task, each team was allowed to submit five runs offline and another five online via the BioCreative Meta-Server. From the 52 runs submitted for the ACT, the highest Matthew's Correlation Coefficient (MCC) score measured was 0.55 at an accuracy of 89% and the best AUC iP/R was 68%. Most ACT teams explored machine learning methods, some of them also used lexical resources like MeSH terms, PSI-MI concepts or particular lists of verbs and nouns, some integrated NER approaches. For the IMT, a total of 42 runs were evaluated by comparing systems against manually generated annotations done by curators from the BioGRID and MINT databases. The highest AUC iP/R achieved by any run was 53%, the best MCC score 0.55. In case of competitive systems with an acceptable recall (above 35%) the macro-averaged precision ranged between 50% and 80%, with a maximum F-Score of 55%.


The results of the ACT task of BioCreative III indicate that classification of large unbalanced article collections reflecting the real class imbalance is still challenging. Nevertheless, text-mining tools that report ranked lists of relevant articles for manual selection can potentially reduce the time needed to identify half of the relevant articles to less than 1/4 of the time when compared to unranked results. Detecting associations between full text articles and interaction detection method PSI-MI terms (IMT) is more difficult than might be anticipated. This is due to the variability of method term mentions, errors resulting from pre-processing of articles provided as PDF files, and the heterogeneity and different granularity of method term concepts encountered in the ontology. However, combining the sophisticated techniques developed by the participants with supporting evidence strings derived from the articles for human interpretation could result in practical modules for biological annotation workflows.