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• W2809009215 abstract "Drug-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." @default.
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• W2809009215 date "2018-06-18" @default.
• W2809009215 modified "2023-10-16" @default.
• W2809009215 title "Pattern Discovery from High-Order Drug-Drug Interaction Relations" @default.
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• W2809009215 doi "https://doi.org/10.1007/s41666-018-0020-2" @default.
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