Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2015 Apr 15;31(8):1258-66.
doi: 10.1093/bioinformatics/btu795. Epub 2014 Nov 29.

Computer-assisted Curation of a Human Regulatory Core Network From the Biological Literature

Affiliations

Computer-assisted Curation of a Human Regulatory Core Network From the Biological Literature

Philippe Thomas et al. Bioinformatics. .

Abstract

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 45 000 sentences potentially describing such relationships. We ranked these sentences by a machine-learning approach. The top-2500 sentences contained ∼900 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 http://fastforward.sys-bio.net/.

Contact: leser@informatik.hu-berlin.de or nils.bluethgen@charite.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Similar articles

See all similar articles

Cited by 4 articles

Publication types

Substances

LinkOut - more resources

Feedback