Treffer: Pattern Discovery from High-Order Drug-Drug Interaction Relations

Title:
Pattern Discovery from High-Order Drug-Drug Interaction Relations
Authors:
Lang Li, Wen-Hao Chiang, Titus Schleyer, Li Shen, Xia Ning
Contributors:
Computer and Information Science, School of Science
Source:
Journal of Healthcare Informatics Research. 2:272-304
Publisher Information:
Springer Science and Business Media LLC, 2018.
Publication Year:
2018
Subject Terms:
0301 basic medicine, 0303 health sciences, 03 medical and health sciences, Drug-drug similarities, Drug-drug interactions, Stochastic algorithm, Graph representation, Convolution, 3. Good health
Document Type:
Fachzeitschrift Article<br />Other literature type
File Description:
application/pdf
Language:
English
ISSN:
2509-498X
2509-4971
DOI:
10.1007/s41666-018-0020-2
Access URL:
https://pubmed.ncbi.nlm.nih.gov/35415408
http://doi.org/10.1007/s41666-018-0020-2
https://dblp.uni-trier.de/db/journals/jhir/jhir2.html#ChiangSSLN18
https://link.springer.com/article/10.1007/s41666-018-0020-2
https://doi.org/10.1007/s41666-018-0020-2
Rights:
Springer TDM
Accession Number:
edsair.doi.dedup.....63228c0ce2fb95de1cfd48667994a306
Database:
OpenAIRE

Weitere Informationen

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