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Journal of Healthcare Informatics Research

, Volume 2, Issue 3, pp 272–304 | Cite as

Pattern Discovery from High-Order Drug-Drug Interaction Relations

  • Wen-Hao Chiang
  • Titus Schleyer
  • Li Shen
  • Lang Li
  • Xia NingEmail author
Research Article
  • 561 Downloads

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

Keywords

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

Notes

Acknowledgments

This publication was made possible by the Lilly Endowment, Inc. Physician Scientist Initiative.

Funding information

This material is based upon work supported by the National Science Foundation under Grant Number IIS-1622526. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.

Supplementary material

41666_2018_20_MOESM1_ESM.pdf (458 kb)
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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer & Information ScienceIndiana University - Purdue University IndianapolisIndianapolisUSA
  2. 2.Center for Biomedical InformaticsRegenstrief InstituteIndianapolisUSA
  3. 3.Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPennsylvaniaUSA
  4. 4.Department of Biomedical InformaticsThe Ohio State UniversityColumbusUSA
  5. 5.Center for Computational Biology and BioinformaticsIndiana University School of MedicineIndianapolisUSA

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