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This article is part of the supplement: A critical assessment of text mining methods in molecular biology

Open Access Highly Accessed Introduction

Overview of BioCreAtIvE: critical assessment of information extraction for biology

Lynette Hirschman1*, Alexander Yeh1, Christian Blaschke2 and Alfonso Valencia3

Author affiliations

1 The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA

2 Bioalma, Ronda de Poniente, 4 – 2nd floor, Unit C-D 28760 Tres Cantos, Madrid, Spain

3 Protein Design Group, National Center of Biotechnology, CNB-CSIC, Cantoblanco, E-28049 Madrid, Spain

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Citation and License

BMC Bioinformatics 2005, 6(Suppl 1):S1  doi:10.1186/1471-2105-6-S1-S1

Published: 24 May 2005

Abstract

Background

The goal of the first BioCreAtIvE challenge (Critical Assessment of Information Extraction in Biology) was to provide a set of common evaluation tasks to assess the state of the art for text mining applied to biological problems. The results were presented in a workshop held in Granada, Spain March 28–31, 2004. The articles collected in this BMC Bioinformatics supplement entitled "A critical assessment of text mining methods in molecular biology" describe the BioCreAtIvE tasks, systems, results and their independent evaluation.

Results

BioCreAtIvE focused on two tasks. The first dealt with extraction of gene or protein names from text, and their mapping into standardized gene identifiers for three model organism databases (fly, mouse, yeast). The second task addressed issues of functional annotation, requiring systems to identify specific text passages that supported Gene Ontology annotations for specific proteins, given full text articles.

Conclusion

The first BioCreAtIvE assessment achieved a high level of international participation (27 groups from 10 countries). The assessment provided state-of-the-art performance results for a basic task (gene name finding and normalization), where the best systems achieved a balanced 80% precision / recall or better, which potentially makes them suitable for real applications in biology. The results for the advanced task (functional annotation from free text) were significantly lower, demonstrating the current limitations of text-mining approaches where knowledge extrapolation and interpretation are required. In addition, an important contribution of BioCreAtIvE has been the creation and release of training and test data sets for both tasks. There are 22 articles in this special issue, including six that provide analyses of results or data quality for the data sets, including a novel inter-annotator consistency assessment for the test set used in task 2.