The average distance between a disease drug of known indications is 3. 75, a finding concurred by previous reports. These preliminary analyses, and our previous studies with rare disease networks where we noted that the relation ship between diseases cannot be fully captured by the genes network alone, http://www.selleckchem.com/products/Imatinib(STI571).html motivated us to build a feature based functional connectivity map between diseases and drugs. Disease disease, drug drug, and disease drug pairs edge pruning and weighted heterogeneous network generation Using the disease gene, drug target, and the enriched fea tures of diseases and drugs, we built a gene and feature based network where nodes represent disease or drug while the edges represent shared gene and/or enriched features. We used Jaccard score to measure the feature similarity between each pair of the nodes.

In order to retain only edges that represent significant potentially significant relationships, we used a cutoff of 0. 5 on Jaccard indexes across the four networks. Thus, the final network contained edges which were a union of pairs that passed the 0. 5 Jaccard score threshold in each individual category. Based on whether a pair of nodes shares genes or enriched fea tures or both, we assigned weights to all the edges in the filtered pairs. For instance, a pair of nodes with a weighted edge of 1 indicates that they share either a gene or one of the three features whereas a weight of 4 indi cates that the two nodes showed significant associations. The resulting weighted heterogeneous network consisted of 657 disease nodes and 3489 drug nodes.

The total num ber of edges in this network is 116493. 680 edges were between two diseases, 1626 were between a disease drug and 114187 between two drugs. Modularity analyses of the disease drug network We used two graph clustering algorithms to detect dis ease Batimastat drug modules in this weighted heterogeneous net work of diseases and drugs. Using Louvains method, we could identify 293 modules. Of these, 98 modules com prised nodes of both diseases and drugs. Using Cluster ONE, we were able to partition the disease drug heterogeneous network into 312 clusters, of which, 110 clusters comprised both diseases and drugs. Using the ClusterONE and Louvain detected commu nities we generated all possible disease drug combina tions on a per cluster basis. We call these the drug repositioning candidates . To test the robustness of these novel drug repositioning candidate pairs, we removed 10% of the edges at a time and calculated the recovery rate of our predictions in a repetitive Volasertib order manner. Briefly, in each run, we randomly removed 10% of edges from the heterogeneous weighted disease drug network and performed graph clustering to detect the communities and extract drug repositioning candidate pairs.