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Drug combinations can cause adverse drug-drug interactions(DDIs).
An elementary mathematical theory of classification and prediction, ibm report (november, 1958), cited in: G. salton, automatic information organization and retrieval, 1968
Taffee T Tanimoto · 1968
Earlier work this paper cites.
When good drugs go bad
Kathleen M. Giacomini, Ronald M. Krauss, Dan M. Roden, et al · 2007
Earlier work this paper cites.
A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports
Nicholas P Tatonetti, Guy Haskin Fernald, and Russ B Altman · 2012
Earlier work this paper cites.
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Earlier work this paper cites.
Adam: A method for stochastic optimization
DiederikP. Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Molecular similarity in medicinal chemistry
Gerald Maggiora, Martin Vogt, Dagmar Stumpfe, and Jürgen Bajorath · 2014
Earlier work this paper cites.
Similarity-based modeling in large-scale prediction of drug-drug interactions
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Earlier work this paper cites.
A novel multiple-stage antimalarial agent that inhibits protein synthesis
Beatriz Baragaña, Irene Hallyburton, Marcus C. S. Lee, et al · 2015
Earlier work this paper cites.
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Earlier work this paper cites.
Synergistic drug combinations for cancer identified in a crispr screen for pairwise genetic interactions
Kyuho Han, Edwin E Jeng, Gaelen T Hess, et al · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
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Combination cancer therapy can confer benefit via patient-to-patient variability without drug additivity or synergy
Adam C. Palmer and Peter K. Sorger · 2017
Earlier work this paper cites.
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio’, and Yoshua Bengio · 2017
Earlier work this paper cites.
Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning
Andrej Kastrin, Polonca Ferk, and Brane Leskošek · 2018
Earlier work this paper cites.
Drug similarity integration through attentive multi-view graph auto-encoders
Tengfei Ma, Cao Xiao, Jiayu Zhou, et al · 2018
Earlier work this paper cites.
Drug similarity integration through attentive multi-view graph auto-encoders
Tengfei Ma, Cao Xiao, Jiayu Zhou, and Fei Wang · 2018
Earlier work this paper cites.
Deep learning improves prediction of drug-drug and drug-food interactions
Jae Yong Ryu, Hyun Uk Kim, and Sang Yup Lee · 2018
Earlier work this paper cites.
Modeling Relational Data with Graph Convolutional Networks
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2018
Earlier work this paper cites.
Drug-drug adverse effect prediction with graph co-attention
Andreea Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò, and Jian Tang · 2019
Earlier work this paper cites.
Language models are few-shot learners
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Muffin: multi-scale feature fusion for drug–drug interaction prediction
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Arnold K Nyamabo, Hui Yu, Zun Liu, et al · 2022
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Drug–drug interaction prediction with learnable size-adaptive molecular substructures
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Gppt: Graph pre-training and prompt tuning to generalize graph neural networks
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Molecular substructure-aware network for drug-drug interaction prediction
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Integrating heterogeneous knowledge graphs into drug–drug interaction extraction from the literature
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Prompt learning on temporal interaction graphs
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