Background: Similar diseases are always caused by similar molecular origins, such as diasease-related protein-coding
genes (PCGs). And the molecular associations reflect their similarity. Therefore, current methods for calculating disease
similarity often utilized functional interactions of PCGs. Besides, the existing methods have neglected a fact that genes
could also be associated in the gene functional network (GFN) based on intermediate nodes.
Methods: Here we presented a novel method, InfDisSim, to deduce the similarity of diseases. InfDisSim utilized the
whole network based on random walk with damping to model the information flow. A benchmark set of similar
disease pairs was employed to evaluate the performance of InfDisSim.
Results: The region beneath the receiver operating characteristic curve (AUC) was calculated to assess the performance.
As a result, InfDisSim reaches a high AUC (0.9786) which indicates a very good performance. Furthermore, after
calculating the disease similarity by the InfDisSim, we reconfirmed that similar diseases tend to have common
therapeutic drugs (Pearson correlation γ2 = 0.1315, p = 2.2e-16). Finally, the disease similarity computed by infDisSim
was employed to construct a miRNA similarity network (MSN) and lncRNA similarity network (LSN), which were further
exploited to predict potential associations of lncRNA-disease pairs and miRNA-disease pairs, respectively. High AUC
(0.9893, 0.9007) based on leave-one-out cross validation shows that the LSN and MSN is very appropriate for predicting
novel disease-related lncRNAs and miRNAs, respectively.
Conclusions: The high AUC based on benchmark data indicates the method performs well. The method is valuable in
the prediction of disease-related lncRNAs and miRNAs.

• Measuring disease similarity and predicting disease-related ncRNAs by a novel method