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Abstract
The Transporter Classification Database (TCDB; http://www.tcdb.org) serves as a common reference point for transport protein research. The database contains more than 10 000 non-redundant proteins that represent all currently recognized families of transmembrane molecular transport systems. Proteins in TCDB are organized in a five level hierarchical system, where the first two levels are the class and subclass, the second two are the family and subfamily, and the last one is the transport system. Superfamilies that contain multiple families are included as hyperlinks to the five tier TC hierarchy. TCDB includes proteins from all types of living organisms and is the only transporter classification system that is both universal and recognized by the International Union of Biochemistry and Molecular Biology. It has been expanded by manual curation, contains extensive text descriptions providing structural, functional, mechanistic and evolutionary information, is supported by unique software and is interconnected to many other relevant databases. TCDB is of increasing usefulness to the international scientific community and can serve as a model for the expansion of database technologies. This manuscript describes an update of the database descriptions previously featured in NAR database issues.
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Affiliation(s)
- Milton H Saier
- Department of Molecular Biology, University of California at San Diego, La Jolla, CA 92093-0116, USA
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Krallinger M, Vazquez M, Leitner F, Salgado D, Chatr-aryamontri A, Winter A, Perfetto L, Briganti L, Licata L, Iannuccelli M, Castagnoli L, Cesareni G, Tyers M, Schneider G, Rinaldi F, Leaman R, Gonzalez G, Matos S, Kim S, Wilbur WJ, Rocha L, Shatkay H, Tendulkar AV, Agarwal S, Liu F, Wang X, Rak R, Noto K, Elkan C, Lu Z, Dogan RI, Fontaine JF, Andrade-Navarro MA, Valencia A. The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text. BMC Bioinformatics 2011; 12 Suppl 8:S3. [PMID: 22151929 PMCID: PMC3269938 DOI: 10.1186/1471-2105-12-s8-s3] [Citation(s) in RCA: 107] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND 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. RESULTS 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%. CONCLUSIONS 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.
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Affiliation(s)
- Martin Krallinger
- Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Miguel Vazquez
- Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Florian Leitner
- Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - David Salgado
- Australian Regenerative Medicine Institute, Monash University, Australia
| | | | - Andrew Winter
- School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - Livia Perfetto
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | | | - Luana Licata
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | | | - Luisa Castagnoli
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
| | - Gianni Cesareni
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
- IRCSS, Fondazione Santa Lucia, Rome, Italy
| | - Mike Tyers
- School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - Gerold Schneider
- Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | - Fabio Rinaldi
- Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | - Robert Leaman
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, USA
| | - Graciela Gonzalez
- Department of Biomedical Informatics, Arizona State University, Tempe, Arizona, USA
| | - Sergio Matos
- Institute of Electronics and Telematics Engineering of Aveiro, University of Aveiro Campus Universitario de Santiago, 3810-193 Aveiro, Portugal
| | - Sun Kim
- National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, Maryland, 20894, USA
| | - W John Wilbur
- National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, Maryland, 20894, USA
| | - Luis Rocha
- School of Informatics and Computing, Indiana University, 919 E. 10th St Bloomington IN, 47408, USA
| | - Hagit Shatkay
- Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA
| | - Ashish V Tendulkar
- Department of Computer Science and Engineering, IIT Madras, Chennai-600 036, India
| | - Shashank Agarwal
- Medical Informatics, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA
| | - Feifan Liu
- Medical Informatics, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA
| | - Xinglong Wang
- National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester, UK
| | - Rafal Rak
- National Centre for Text Mining and School of Computer Science, University of Manchester, Manchester, UK
| | - Keith Noto
- Department of Computer Science, Tufts University, 161 College Ave, Medford, MA 02155, USA
| | - Charles Elkan
- Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, Maryland, 20894, USA
| | - Rezarta Islamaj Dogan
- National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, Maryland, 20894, USA
| | - Jean-Fred Fontaine
- Computational Biology and Data Mining Group, Max-Delbrück-Centrum für Molekulare Medizin, Robert-Rössle-Str. 10, 13125 Berlin, Germany
| | - Miguel A Andrade-Navarro
- Computational Biology and Data Mining Group, Max-Delbrück-Centrum für Molekulare Medizin, Robert-Rössle-Str. 10, 13125 Berlin, Germany
| | - Alfonso Valencia
- Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
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