Abstract
Discovering similar diseases is very helpful for revealing the pathogenesis of diseases and making direction in drug use. And related diseases are often triggered by disease-related genes. Therefore, function interaction networks structured by disease-related genes are suitable for measurement of disease similarity, and some methods have utilized the advantage of function interaction of disease-related genes. However, all of them were developed by using a single gene functional network, some of them ignoring the effect of non-neighbour nodes in a functional interaction network. In this study, we propose a new method, FNSemSim, for computing relatedness between diseases by fusing two protein networks, which could be utilized fully based on random walk with restart (RWR). And a benchmark set of similar disease pairs are used to assess the performance of FNSemSim. As a result, FNSemSim achieved a very good performance with a high AUC (area under the receiver operating characteristic curve) reached 98.7%. Furthermore, we further studied the impact of different data sources, including function interaction networks and disease-related genes databases. It was found that the quality of the data sources has a greater impact on the performance of disease similarity calculation than the size of the data source, and utilizing function interaction networks and gene-disease association data could improve the performance of FNSemSim.