title: Literature mining, gene-set enrichment and pathway analysis for target identification in Behçet's disease.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  Text mining methods combined with gene enrichment tools, pathway analysis and causal analysis algorithms.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors: Computational Biology, GlaxoSmithKline Medicine Research Centre, Herts, UK.
authors: Paul
authors: P
authors: Wilson
authors: Computational Biology, GlaxoSmithKline Medicine Research Centre, Herts, UK.
authors: Christopher
authors: C
authors: Larminie
authors: Department of Renal Medicine, Box 57, Addenbrooke's Hospital, Cambridge, UK. ronasmith@doctors.org.uk.
authors: Rona
authors: R
authors: Smith
mesh_terms: D000465:Algorithms; D001528:Behcet Syndrome / Q000175:diagnosis / Q000235:genetics* / Q000276:immunology / Q000628:therapy; D019295:Computational Biology; D057225:Data Mining; D030541:Databases, Genetic; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease; D006801:Humans; D060889:Inducible T-Cell Co-Stimulator Protein / Q000235:genetics / Q000276:immunology; D020382:Interleukin-18 / Q000235:genetics / Q000276:immunology; D053473:Nod2 Signaling Adaptor Protein / Q000235:genetics / Q000276:immunology; D010641:Phenotype; D060066:Protein Interaction Maps; D015398:Signal Transduction
chemical_list: D005819:Genetic Markers; C556692:ICOS protein, human; D060889:Inducible T-Cell Co-Stimulator Protein; D020382:Interleukin-18; C429018:NOD2 protein, human; D053473:Nod2 Signaling Adaptor Protein