“Who are the key players behind a disease state?”: Outcomes of a new computational approach on cancer data

Abstract

The problem of identifying disease causing genes and dysregulated pathways has attained a key position in computational biology research, as it helps in understanding major causal genes and their interactions behind a disease state and thereby enables proposing new drug targets. The development of computational approaches for the inference of disease causing genes and associated pathways can improve the accuracy and efficiency and reduce the cost of biomedical analysis. Identification of disease causing genes from the large set of genes produced by high throughput experiments is a time consuming and costly process. Based on the fact that interactions among several genes results in certain phenotypes, the molecular interaction network is a major resource for computational approaches to identify disease causing genes and associated pathways. Executing computations on the huge molecular interaction network is also major challenge. Here, we address the problem of inferring disease causing genes and their pathways using graph theoretical approaches which focus on reducing the execution time by using graph pruning techniques, without compromising on accuracy of results. Experimentation on real biological data shows reduced execution time and increased accuracy than other methods reported in literature on benchmark datasets, on using the proposed technique.