Pattern Discovery from Directional High-Order Drug-Drug Interaction Relations

Drug-Drug Interactions (DDIs) and associated Adverse Drug Reactions (ADRs) represent a significant public health problem in the United States. The research presented in this paper tackles the problems of representing, discovering, quantifying and visualizing patterns from high-order DDIs in a purely data-driven fashion. We formulate the problems based on a notion of directional DDI relations and correspondingly developed weighted hyper-graphlets for their representation. We also develop a convolutional scheme and its stochastic algorithm SD3ID2S to learn the directional DDI based drug-drug similarities. Our experimental results demonstrate that such approaches can well capture the patterns from high-order DDIs.