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Updated: Sep 27, 2025

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
Published on: May 21, 2018
Pattern Discovery from High-Order Drug-Drug Interaction Relations
Wen-Hao Chiang1, Titus Schleyer2, Li Shen3
1Department of Computer & Information Science, Indiana University - Purdue University Indianapolis, Indianapolis, IN 46202 USA.
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 to discover DDI-based drug-drug similarities. Our experimental results demonstrate that such approaches can well capture the patterns of high-order DDIs.
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