Graph pruning based approach for inferring disease causing genes and associated pathways

Abstract

The problem of inferring disease causing genes and dysregulated pathways has obtained a vital position in computational biology research. But, the huge size of the biological network makes this process computationally challenging. Here, we tackle the problem of inferring disease causing genes and associated pathways using graph pruning techniques which focus on the improvement in accuracy of results in reasonable execution time and fetching more causal genes and their pathways. Experimentation of the proposed approach and the reported approaches in literature was done on real biological data. More efficient results in terms of accuracy and execution time based on benchmark datasets were obtained as its outcome. If the function of the newly identified genes/pathways in the disease states could be validated biologically, for any unknown influences in the disease development, it would significantly affect our effort to design new drug targets and defeat the diseases.