Computational drug repositioning through heterogeneous network clustering by Wu C, Gudivada RC, Aronow BJ, Jegga AG. (BMC Syst Biol. 2013;7 Suppl 5:S6. doi: 10.1186/1752-0509-7-S5-S6. Epub 2013 Dec 9.)
Given the costly and time consuming process and high attrition rates in drug discovery and development, drug repositioning or drug repurposing is considered as a viable strategy both to replenish the drying out drug pipelines and to surmount the innovation gap. Although there is a growing recognition that mechanistic relationships from molecular to systems level should be integrated into drug discovery paradigms, relatively few studies have integrated information about heterogeneous networks into computational drug-repositioning candidate discovery platforms.
Using known disease-gene and drug-target relationships from the KEGG database, we built a weighted disease and drug heterogeneous network. The nodes represent drugs or diseases while the edges represent shared gene, biological process, pathway, phenotype or a combination of these features. We clustered this weighted network to identify modules and then assembled all possible drug-disease pairs (putative drug repositioning candidates) from these modules. We validated our predictions by testing their robustness and evaluated them by their overlap with drug indications that were either reported in published literature or investigated in clinical trials.
Previous computational approaches for drug repositioning focused either on drug-drug and disease-disease similarity approaches whereas we have taken a more holistic approach by considering drug-disease relationships also. Further, we considered not only gene but also other features to build the disease drug networks. Despite the relative simplicity of our approach, based on the robustness analyses and the overlap of some of our predictions with drug indications that are under investigation, we believe our approach could complement the current computational approaches for drug repositioning candidate discovery.
A reminder that data clustering isn’t just of academic interest but is useful in highly remunerative fields as well. 😉
There is a vast amount of literature on data clustering but I don’t know if there is a collection of data clustering patterns?
That is a work that summarizes where data clustering has been used by domain and the similarities on which clustering was performed.
In this article, the clustering was described as:
The nodes represent drugs or diseases while the edges represent shared gene, biological process, pathway, phenotype or a combination of these features.
Has that been used elsewhere in medical research?
Not that clustering should be limited to prior patterns but prior patterns could stimulate new patterns to be applied.