selleck chemical Dorsomorphin However, most of these approaches were either drug centric or disease centric and not indications centric . In other words, few stu dies have used a direct disease drug centric approach. While there have been studies using heterogeneous net works for drug repositioning, to the best of our knowledge there have been no previous reports that undertook a direct analysis of heterogeneous disease drug network and used network clustering based approaches on heterogeneous networks to identify drug repositioning candidates. In the current study, we built a gene and feature based disease and drug heterogeneous network and applied network clustering to identify drug repositioning candidates. We used two state of art network clustering approaches to identify the modules of diseases drugs.

We validated the robustness of our methodology by removing ten percent of the edges and calculating the recovery rate of our predictions. Finally, we performed a literature and clinical trials data search to check for potential overlap of our discovered novel indications. Methods Disease gene and drug gene associations Known disease gene and drug target associations were downloaded from KEGG Medicus, There were a total of 1301 diseases and 3613 drugs with at least one known gene association along with 1976 known indi cations. To augment the drug targets, we also used drug target data from DrugBank using KeggDrug DrugBank mappings. Generation of disease disease, drug drug, and disease drug pairs based on shared genes or features The nodes in our network are diseases and drugs while the edges represent either a shared gene or a shared fea ture.

We first built a gene based network where two nodes are connected if they share a gene. We used Jaccard coefficient to mea sure the similarity between two nodes. Because a disease or AV-951 drug can be related to other dis ease or drug even if they do not share a gene, we further the site enhanced our network by adding edges that represent shared features. To do this, we first performed an enrichment analyses of each of the disease and drug using ToppFun application of the ToppGene Suite. For each of disease and drug, we first computed the enriched biological processes, pathways, and mouse phe notype. We then built a feature based network where nodes represent disease or drug while the edges repre sent shared enriched features. We used Jaccard score to measure the feature similarity between each pair of the nodes. We thereby generated a list of disease disease, drug drug, and dis ease drug pairs based on shared genes and/or enriched features.