Computer-assisted curation of a human regulatory core network from the biological literature

Philippe Thomas, Pawel Durek, Illés Solt, Bertram Klinger, Franziska Witzel, Pascal Schulthess, Yvonne Mayer, Domonkos Tikk, Nils Blüthgen, Ulf Leser

Research output: Contribution to journalArticle

4 Citations (Scopus)


Motivation: A highly interlinked network of transcription factors (TFs) orchestrates the context-dependent expression of human genes. ChIP-chip experiments that interrogate the binding of particular TFs to genomic regions are used to reconstruct gene regulatory networks at genome-scale, but are plagued by high false-positive rates. Meanwhile, a large body of knowledge on high-quality regulatory interactions remains largely unexplored, as it is available only in natural language descriptions scattered over millions of scientific publications. Such data are hard to extract and regulatory data currently contain together only 503 regulatory relations between human TFs. Results: We developed a text-mining-assisted workflow to systematically extract knowledge about regulatory interactions between human TFs from the biological literature. We applied this workflow to the entire Medline, which helped us to identify more than 45â ‰000 sentences potentially describing such relationships. We ranked these sentences by a machine-learning approach. The top-2500 sentences contained â 1/4900 sentences that encompass relations already known in databases. By manually curating the remaining 1625 top-ranking sentences, we obtained more than 300 validated regulatory relationships that were not present in a regulatory database before. Full-text curation allowed us to obtain detailed information on the strength of experimental evidences supporting a relationship. Conclusions: We were able to increase curated information about the human core transcriptional network by >60% compared with the current content of regulatory databases. We observed improved performance when using the network for disease gene prioritization compared with the state-of-the-art. Availability and implementation: Web-service is freely accessible at Contact:

Original languageEnglish
Pages (from-to)1258-1266
Number of pages9
Issue number8
Publication statusPublished - Apr 15 2015


ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

Thomas, P., Durek, P., Solt, I., Klinger, B., Witzel, F., Schulthess, P., Mayer, Y., Tikk, D., Blüthgen, N., & Leser, U. (2015). Computer-assisted curation of a human regulatory core network from the biological literature. Bioinformatics, 31(8), 1258-1266.