Literature mining, gene-set enrichment and pathway analysis for target identification in Behçet’s disease

1, 2, 3

  1. Computational Biology, GlaxoSmithKline Medicine Research Centre, Herts, UK.
  2. Computational Biology, GlaxoSmithKline Medicine Research Centre, Herts, UK.
  3. Department of Renal Medicine, Box 57, Addenbrooke’s Hospital, Cambridge, UK. ronasmith@doctors.org.uk

CER9656 Submission on line
2016 Vol.34, N°6 ,Suppl.102 - PI 0101, PF 0110
Full Papers

Rheumatology Article



To use literature mining to catalogue Behçet’s associated genes, and advanced computational methods to improve the understanding of the pathways and signalling mechanisms that lead to the typical clinical characteristics of Behçet’s patients. To extend this technique to identify potential treatment targets for further experimental validation.
Text mining methods combined with gene enrichment tools, pathway analysis and causal analysis algorithms.
This approach identified 247 human genes associated with Behçet’s disease and the resulting disease map, comprising 644 nodes and 19220 edges, captured important details of the relationships between these genes and their associated pathways, as described in diverse data repositories. Pathway analysis has identified how Behçet’s associated genes are likely to participate in innate and adaptive immune responses. Causal analysis algorithms have identified a number of potential therapeutic strategies for further investigation.
Computational methods have captured pertinent features of the prominent disease characteristics presented in Behçet’s disease and have highlighted NOD2, ICOS and IL18 signalling as potential therapeutic strategies.

PMID: 27791955 [PubMed]

Received: 13/06/2016 - Accepted : 07/09/2016 - In Press: 18/10/2016 - Published: 25/10/2016