Automatic reconstruction of a bacterial regulatory network using Natural Language Processing

Background: Manual curation of biological databases, an expensive and labor-intensive process, is essential for high quality integrated data. In this paper we report the implementation of a stateof-the-art Natural Language Processing system that creates computer-readable networks of ry interactions directly from different collections of abstracts and full-text papers. Our major aim is to understand how automatic annotation using Text-Mining techniques can complement manual curation of biological databases. We implemented a rule-based system to generate networks from different sets of documents dealing with regulation in Escherichia coli K-12. Results: Performance evaluation is based on the most comprehensive transcriptional regulation database for any organism, the manually-curated RegulonDB, 45% of which we were able to recreate automatically. From our automated analysis we were also able to find some new interactions from papers not already curated, or that were missed in the manual...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Carlos Rodríguez Penagos, Heladia Salgado, Irma Martínez-Flores, Julio Collado-Vides
• Keywords: Biological Databases, BMCBI 2007, Comprehensive Transcriptional Regulation, Manual Curation