Pattern Discovery from High-Order Drug-Drug Interaction Relations

Wen Hao Chiang, Titus Schleyer, Li Shen, Lang Li, Xia Ning

Research output: Contribution to journalArticlepeer-review

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 SD 2ID 2S to discover DDI-based drug-drug similarities. Our experimental results demonstrate that such approaches can well capture the patterns of high-order DDIs.

Original languageEnglish (US)
Pages (from-to)272-304
Number of pages33
JournalJournal of Healthcare Informatics Research
Volume2
Issue number3
DOIs
StatePublished - Sep 1 2018

Keywords

  • Convolution
  • Drug-drug interactions
  • Drug-drug similarities
  • Graph representation
  • Stochastic algorithm

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Information Systems
  • Artificial Intelligence

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