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Prediction of Drug Interactions Using Graph-Topological Features and GNN
Navyasree Balamuralidhar
, Pranav Surendran
, Gaurav Singh
*
, Shrutilipi Bhattacharjee
,
Ramya D. Shetty
*
Corresponding author for this work
School of Computer Engineering
Research output
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Chapter in Book/Report/Conference proceeding
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Conference contribution
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Citation (Scopus)
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INIS
interactions
100%
topology
100%
prediction
100%
graphs
100%
drugs
80%
side effects
80%
clinical trials
40%
learning
40%
levels
20%
risks
20%
cost
20%
patients
20%
safety
20%
data
20%
correlations
20%
information
20%
compatibility
20%
constraints
20%
datasets
20%
Engineering
Nodes
100%
Computational Complexity
100%
Additional Feature
100%
Deep Learning Method
100%
Computer Science
Topological Feature
100%
Computational Complexity
50%
Centrality Measure
50%
Massive Amount
50%
Deep Learning Method
50%
Pharmacology, Toxicology and Pharmaceutical Science
Side Effect
100%
Clinical Trial
50%
Drug-Drug Interaction
50%
Biochemistry, Genetics and Molecular Biology
Clinical Trial
100%
Drug Interaction
100%
Neuroscience
Drug Interaction
50%