InfDisSim: A novel method for measuring disease similarity based on information flow

Similar diseases are often caused by their similar molecular origins, such as disease-related protein-coding genes (PCGs). And nowadays, the function of PCGs has been widely studied on a gene function network, where each node represents a gene and each edge indicates an interaction between pair-wise genes. Therefore, functional interaction between disease-related PCGs should be exploited to measure disease similarity. Actually, functional interaction of pair-wise PCGs has been introduced to calculate disease similarity recently. However, existing method ignores that genes could also be associated based on intermediate nodes in the gene functional network. Here, in this article, we proposed a novel method, InfDisSim, to infer disease similarity. InfDisSim models the information flow to the network based on random walk with damping, in which the entire network could be fully utilized. The performance of InfDisSim was evaluated by a benchmark set of similar disease pairs. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the performance. As a result, InfDisSim achieves a very high AUC (0.9786), which shows it performs well. Furthermore, based on the disease similarity computed by the infDisSim, we re-validated that similar diseases tend to have common therapeutic drugs (Pearson correlation γ
²
=0.1315, p=2.2e-16). Finally, InfDisSim disease similarity was exploited to construct a lncRNA similarity network (LSN), which was further applied to predict potential associations between diseases and lncRNAs. High AUC (0.9893) based on leave-one-out cross validation shows the LSN is very suitable for identifying novel disease-related lncRNAs.