Chiang, Wen-HaoSchleyer, TitusShen, LiLi, LangNing, Xia2023-06-052023-06-052018-06-18Chiang WH, Schleyer T, Shen L, Li L, Ning X. Pattern Discovery from High-Order Drug-Drug Interaction Relations. J Healthc Inform Res. 2018;2(3):272-304. Published 2018 Jun 18. doi:10.1007/s41666-018-0020-2https://hdl.handle.net/1805/33499Drug-drug interactions (DDIs) and associated adverse drug reactions (ADRs) represent a significant public health problem in the USA. The research presented in this manuscript tackles the problems of representing, quantifying, discovering, and visualizing patterns from high-order DDIs in a purely data-driven fashion within a unified graph-based framework and via unified convolution-based algorithms. We formulate the problem based on the notions of nondirectional DDI relations (DDI-nd's) and directional DDI relations (DDI-d's), and correspondingly developed weighted complete graphs and hyper-graphlets for their representation, respectively. We also develop a convolutional scheme and its stochastic algorithm SD2ID2S to discover DDI-based drug-drug similarities. Our experimental results demonstrate that such approaches can well capture the patterns of high-order DDIs.en-USPublisher PolicyDrug-drug interactionsDrug-drug similaritiesGraph representationConvolutionStochastic algorithmArticle