Estimating a ranked list of human hereditary diseases for clinical phenotypes by using weighted bipartite network

With the availability of the huge medical knowledge data on the Internet such as the human disease network, protein-protein interaction (PPI) network, and phenotypegene, gene-disease bipartite networks, it becomes practical to help doctors by suggesting plausible hereditary diseases for a set of clinical phenotypes. However, identifying candidate diseases that best explain a set of clinical phenotypes by considering various heterogeneous networks is still a challenging task. In this paper, we propose a new method for estimating a ranked list of plausible diseases by associating phenotypegene with gene-disease bipartite networks. Our approach is to count the frequency of all the paths from a phenotype to a disease through their associated causative genes, and link the phenotype to the disease with path frequency in a new phenotype-disease bipartite (PDB) network. After that, we generate the candidate weights for the edges of phenotypes with diseases in PDB network. We evaluate our proposed method in terms of Normalized Discounted Cumulative Gain (NDCG), and demonstrate that we outperform the previously known disease ranking method called Phenomizer.