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

Open Access Research

MINT and IntAct contribute to the Second BioCreative challenge: serving the text-mining community with high quality molecular interaction data

Andrew Chatr-aryamontri1, Samuel Kerrien2, Jyoti Khadake2, Sandra Orchard2, Arnaud Ceol1, Luana Licata1, Luisa Castagnoli1, Stefano Costa1, Cathy Derow2, Rachael Huntley2, Bruno Aranda2, Catherine Leroy2, Dave Thorneycroft2, Rolf Apweiler2, Gianni Cesareni1* and Henning Hermjakob2*

Author affiliations

1 Department of Biology, University of Rome, Tor Vergata, Via della Ricerca Scientifica, 00133 Rome Italy

2 EMBL - European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SD, UK

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Citation and License

Genome Biology 2008, 9(Suppl 2):S5  doi:10.1186/gb-2008-9-s2-s5

Published: 1 September 2008

Abstract

Background

In the absence of consolidated pipelines to archive biological data electronically, information dispersed in the literature must be captured by manual annotation. Unfortunately, manual annotation is time consuming and the coverage of published interaction data is therefore far from complete. The use of text-mining tools to identify relevant publications and to assist in the initial information extraction could help to improve the efficiency of the curation process and, as a consequence, the database coverage of data available in the literature. The 2006 BioCreative competition was aimed at evaluating text-mining procedures in comparison with manual annotation of protein-protein interactions.

Results

To aid the BioCreative protein-protein interaction task, IntAct and MINT (Molecular INTeraction) provided both the training and the test datasets. Data from both databases are comparable because they were curated according to the same standards. During the manual curation process, the major cause of data loss in mining the articles for information was ambiguity in the mapping of the gene names to stable UniProtKB database identifiers. It was also observed that most of the information about interactions was contained only within the full-text of the publication; hence, text mining of protein-protein interaction data will require the analysis of the full-text of the articles and cannot be restricted to the abstract.

Conclusion

The development of text-mining tools to extract protein-protein interaction information may increase the literature coverage achieved by manual curation. To support the text-mining community, databases will highlight those sentences within the articles that describe the interactions. These will supply data-miners with a high quality dataset for algorithm development. Furthermore, the dictionary of terms created by the BioCreative competitors could enrich the synonym list of the PSI-MI (Proteomics Standards Initiative-Molecular Interactions) controlled vocabulary, which is used by both databases to annotate their data content.