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Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible.
A coefficient of agreement for nominal scales
Jacob Cohen. 1960 · 1960
Earlier work this paper cites.
Extended-connectivity fingerprints
David Rogers and Mathew Hahn. 2010 · 2010
Earlier work this paper cites.
Data-driven prediction of drug effects and interactions
Nicholas P Tatonetti, P Ye Patrick, Roxana Daneshjou, and Russ B Altman. 2012 · 2012
Earlier work this paper cites.
Translating embeddings for modeling multi-relational data. In
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013 · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality. In
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Earlier work this paper cites.
Informatics confronts drug–drug interactions
B. Percha and R. B. Altman. 2013 · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
DeepWalk: Online Learning of Social Representations. In
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014 · 2014
Earlier work this paper cites.
Heterogeneous network edge prediction: a data integration approach to prioritize disease-associated genes
Daniel S Himmelstein and Sergio E Baranzini. 2015 · 2015
Earlier work this paper cites.
Orthostatic hypotension: managing a difficult problem
Pearl K Jones, Brett H Shaw, and Satish R Raj. 2015 · 2015
Earlier work this paper cites.
Large-scale exploration and analysis of drug combinations
Peng Li, Chao Huang, Yingxue Fu, Jinan Wang, Ziyin Wu, Jinlong Ru, Chunli Zheng, Zihu Guo, Xuetong Chen, Wei Zhou, et al · 2015
Earlier work this paper cites.
Line: Large-scale information network embedding. In
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015 · 2015
Earlier work this paper cites.
node2vec: Scalable feature learning for networks. In
Aditya Grover and Jure Leskovec. 2016 · 2016
Earlier work this paper cites.
Complex Embeddings for Simple Link Prediction. In
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Eric Gaussier, and Guillaume Bouchard. 2016 · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2017 · 2017
Earlier work this paper cites.
Learning a health knowledge graph from electronic medical records
Maya Rotmensch, Yoni Halpern, Abdulhakim Tlimat, Steven Horng, and David Sontag. 2017 · 2017
Earlier work this paper cites.
Predicting drug–drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge
Takako Takeda, Ming Hao, Tiejun Cheng, Stephen H Bryant, and Yanli Wang. 2017 · 2017
Earlier work this paper cites.
Predicting rich drug-drug interactions via biomedical knowledge graphs and text jointly embedding
Meng Wang. 2017 · 2017
Cited alongside, same era.
Deep learning improves prediction of drug–drug and drug–food interactions
Jae Yong Ryu, Hyun Uk Kim, and Sang Yup Lee. 2018 · 2018
Cited alongside, same era.
Modeling relational data with graph convolutional networks. In
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018 · 2018
Cited alongside, same era.
Self-Attention with Relative Position Representations. In
Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani. 2018 · 2018
Cited alongside, same era.
Recurrent knowledge graph embedding for effective recommendation
Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Long-Kai Huang, and Chi Xu. 2018 · 2018
Cited alongside, same era.
Deep Graph Infomax. In
Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. 2019 · 2019
Later among the works it cites.
Subgraph Neural Networks
Emily Alsentzer, Samuel G Finlayson, Michelle M Li, and Marinka Zitnik. 2020 · 2020
Closest in time.
Graph Transformer for Graph-to-Sequence Learning. In
Deng Cai and Wai Lam. 2020 · 2020
Closest in time.
Yuanfei Dai, Chenhao Guo, Wenzhong Guo, and Carsten Eickhoff. 2020 · 2020
Closest in time.
Network medicine framework for identifying drug repurposing opportunities for covid-19
Deisy Morselli Gysi, Ítalo Do Valle, Marinka Zitnik, Asher Ameli, Xiao Gan, Onur Varol, Helia Sanchez, Rebecca Marlene Baron, Dina Ghiassian, Joseph Loscalzo, et al · 2020
Closest in time.
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Cited alongside, same era.
Label-free distant supervision for relation extraction via knowledge graph embedding. In
Guanying Wang, Wen Zhang, Ruoxu Wang, Yalin Zhou, Xi Chen, Wei Zhang, Hai Zhu, and Huajun Chen. 2018 · 2018
Cited alongside, same era.
DrugBank 5.0: a major update to the DrugBank database for 2018
David S Wishart, Yannick D Feunang, An C Guo, Elvis J Lo, Ana Marcu, Jason R Grant, Tanvir Sajed, Daniel Johnson, Carin Li, Zinat Sayeeda, et al · 2018
Cited alongside, same era.
Representation Learning on Graphs with Jumping Knowledge Networks. In
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018 · 2018
Cited alongside, same era.
Link prediction based on graph neural networks. In
Muhan Zhang and Yixin Chen. 2018 · 2018
Cited alongside, same era.
Modeling polypharmacy side effects with graph convolutional networks
Marinka Zitnik, Monica Agrawal, and Jure Leskovec. 2018a · 2018
Cited alongside, same era.
Predicting adverse drug-drug interactions with neural embedding of semantic predications
Hannah A Burkhardt, Devika Subramanian, Justin Mower, and Trevor Cohen. 2019 · 2019
Cited alongside, same era.
Kexin Huang, Cao Xiao, Lucas Glass, Marinka Zitnik, and Jimeng Sun. 2020a · 2020
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Graph Meta Learning via Local Subgraphs
Kexin Huang and Marinka Zitnik. 2020 · 2020
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DRKG - Drug Repurposing Knowledge Graph for Covid-19
Vassilis N. Ioannidis, Xiang Song, Saurav Manchanda, Mufei Li, Xiaoqin Pan, Da Zheng, Xia Ning, Xiangxiang Zeng, and George Karypis. 2020 · 2020
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BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision. In
Chen Liang, Yue Yu, Haoming Jiang, Siawpeng Er, Ruijia Wang, Tuo Zhao, and Chao Zhang. 2020 · 2020
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KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction. In
Xuan Lin, Zhe Quan, Zhi-Jie Wang, Tengfei Ma, and Xiangxiang Zeng. 2020 · 2020
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Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network. In
Hongzhi Shi, Quanming Yao, Qi Guo, Yaguang Li, Lingyu Zhang, Jieping Ye, Yong Li, and Yan Liu. 2020 · 2020
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Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification
Rakshith S Srinivasa, Cao Xiao, Lucas Glass, Justin Romberg, and Jimeng Sun. 2020 · 2020
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Network embedding in biomedical data science
Chang Su, Jie Tong, Yongjun Zhu, Peng Cui, and Fei Wang. 2020 · 2020
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Inductive Relation Prediction on Knowledge Graphs. In
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Generalized Multi-Relational Graph Convolution Network
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GraphSAINT: Graph Sampling Based Inductive Learning Method. In
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