The use of gene interaction networks to improve the identification of cancer driver genes.
Bioinformaticians have implemented different strategies to distinguish cancer driver genes from passenger genes. One of the more recent advances uses a pathway-oriented approach. Methods that employ this strategy are highly dependent on the quality and size of the pathway interaction network employed, and require a powerful statistical environment for analyses. A number of genomic libraries are available in R. DriverNet and DawnRank employ pathway-based methods that use gene interaction graphs in matrix form. We investigated the benefit of combining data from 3 different sources on the prediction outcome of cancer driver genes by DriverNet and DawnRank. An enriched dataset was derived comprising 13,862 genes with 372,250 interactions, which increased its accuracy by 17% and 28%, respectively, compared to their original networks. The study identified 33 new candidate driver genes. Our study highlights the potential of combining networks and weighting edges to provide greater accuracy in the identification of cancer driver genes.
Department of Mathematics & Statistics, The Faculty of Science and Technology, The University of the West Indies, St. Augustine Campus , Trinidad and Tobago.
Emilie
E
Ramsahai
Department of Preclinical Sciences, The University of the West Indies, St. Augustine , Trinidad and Tobago.
Kheston
K
Walkins
Department of Mathematics & Statistics, The Faculty of Science and Technology, The University of the West Indies, St. Augustine Campus , Trinidad and Tobago.
Vrijesh
V
Tripathi
Department of Preclinical Sciences, The University of the West Indies, St. Augustine , Trinidad and Tobago.
Melford
M
John
Algorithm; Cancer; Driver genes; Gene expression; Graph; Interaction network; Mutation; Weighted network