Comparison of Disease Specific Sub-Network Identification Programs

Adanur B., GÜNGÖR B.

3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo, Bosnia And Herzegovina, 20 - 23 September 2018, pp.275-280 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/ubmk.2018.8566663
  • City: Sarajevo
  • Country: Bosnia And Herzegovina
  • Page Numbers: pp.275-280
  • Keywords: Active sub-network search, disease associated modules, protein-protein interaction networks, GENOME-WIDE ASSOCIATION, SNP-TARGETED PATHWAYS, CYTOSCAPE APP
  • Abdullah Gül University Affiliated: Yes


Active sub-network search aims to identify a group of interconnected genes in a protein-protein interaction network that contains most of the disease-associated genes. In recent years, to address active sub-network search problem, various algorithms and programs are developed. In this study, performances of disease specific sub-network identification programs are compared. The same input dataset is run in jActiveModules, ActiveSubnetworkGA, CytoHubba, ClusterViz, MCODE, CytoMOBAS, PathFindR, PIN BPA and PEWCC programs. Then, functional enrichment analysis is applied on obtained sub-networks. Finally, they are compared according to the results of GO Enrichment Analysis. In addition to these, work performances, features and requirements of programs are compared.