Ontology-based information extraction of regulatory networks from scientific articles with case studies for Escherichia coli

  • Authors:
  • Antonio Moreno;David Isern;Alejandra C. LóPez Fuentes

  • Affiliations:
  • Universitat Rovira i Virgili, Department of Computer Science and Mathematics, ITAKA research group, Avda. Països Catalans, 26, 43007 Tarragona, Catalonia, Spain;Universitat Rovira i Virgili, Department of Computer Science and Mathematics, ITAKA research group, Avda. Països Catalans, 26, 43007 Tarragona, Catalonia, Spain;Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional, Autónoma de México, Cuernavaca, Morelos, México

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2013

Quantified Score

Hi-index 12.05

Visualization

Abstract

The amount of scientific papers in the Molecular Biology field has experienced an enormous growth in the last years, prompting the need of developing automatic Information Extraction (IE) systems. This work is a first step towards the ontology-based domain-independent generalization of a system that identifies Escherichia coli regulatory networks. First, a domain ontology based on the RegulonDB database was designed and populated. After that, the steps of the existing IE system were generalized to use the knowledge contained in the ontology, so that it could be potentially applied to other domains. The resulting system has been tested both with abstract and full articles that describe regulatory interactions for E. coli, obtaining satisfactory results.