Integrated Edge Information And Pathway Topology For Drug-Disease Associations

The study introduces a novel computational method, iEdgePathDDA, for drug repurposing by focusing on edge information and pathway topology, addressing the limitations of previous methods that overlooked changes in gene interactions. By identifying drug-induced and disease-related edges using the Pearson correlation coefficient and calculating inhibition scores, the method prioritizes drug candidates effectively. Case studies using the CTD database reveal that iEdgePathDDA outperforms existing approaches across five metrics in colorectal, breast, and lung cancer datasets, successfully identifying new drug-disease associations.