The process of finding pharmacogenomic gene-drug associations has greatly improved over the past few decades. Despite this progress, a significant portion of the heritable variation between individuals remains elusive. It has been hypothesized that higher-dimensional phenomena, such as gene-gene-drug interactions, in which variability in multiple genes works together to cause an observable phenotype, could at least partially account for this lack of heritability. However, analytical difficulties brought on by the problem's complexity explosion make it difficult to identify such intricate relationships. We propose a network analysis strategy to make it easier to find such combinatorial pharmacogenetics associations. We specifically looked at the landscape of drug metabolizing enzymes and transporters for all compounds with pharmacogenetic germline labels or dosing guidelines and 100 of the most popular drugs. To picture the quality medication collaboration scene, we utilize multi-faceted scaling to fall this likeness framework into a two-layered network. We propose that the Euclidian distance between nodes can provide information about the likelihood of epistatic interactions, making it possible to use it as a tool to narrow the search space and make it easier to find combinatorial pharmacogenomic associations.
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