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Molecular interaction networks are powerful resources for the discovery.
Interaction between Cyclosporine and Warfarin
Snyder, D. S · 1988
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Product information. clozaril (clozapine)
Novartis, P · 1989
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Chung, F. R. & Graham, F. C · 1997
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Emergence of scaling in random networks
Barabási, A.-L. & Albert, R · 1999
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Product information. targretin (bexarotene)
Ligand, P · 1999
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Product information. coumadin (warfarin)
DuPont, P · 2000
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Birds of a feather: Homophily in social networks
McPherson, M., Smith-Lovin, L. & Cook, J. M · 2001
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Uncovering biological network function via graphlet degree signatures
Milenković, T. & Pržulj, N · 2008
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Visualizing data using t-sne
Maaten, L. v. d. & Hinton, G · 2008
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Similarity index based on local paths for link prediction of complex networks
Lü, L., Jin, C.-H. & Zhou, T · 2009
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The genetic landscape of a cell
Costanzo, M. et al · 2010
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Leveraging social media networks for classification
Tang, L. & Liu, H · 2011
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Link prediction in complex networks: A survey
Lü, L. & Zhou, T · 2011
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A novel link prediction algorithm for reconstructing protein–protein interaction networks by topological similarity
Lei, C. & Ruan, J · 2013
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DeepWalk: Online learning of social representations
Perozzi, B., Al-Rfou, R. & Skiena, S · 2014
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Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties
Cheng, F. & Zhao, Z · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. & Ba, J · 2014
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Product information. belsomra (suvorexant)
Merck, . C. I · 2014
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Uncovering disease-disease relationships through the incomplete interactome
Menche, J. et al · 2015
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Data fusion by matrix factorization
Zitnik, M. & Zupan, B · 2015
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Line: Large-scale information network embedding
Tang, J. et al · 2015
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Label propagation prediction of drug-drug interactions based on clinical side effects
Zhang, P., Wang, F., Hu, J. & Sorrentino, R · 2015
Cited alongside, same era.
Clinical drug-drug interaction assessment of ivacaftor as a potential inhibitor of cytochrome p450 and p-glycoprotein
Robertson, S. M. et al · 2015
Cited alongside, same era.
A global genetic interaction network maps a wiring diagram of cellular function
Costanzo, M. et al · 2016
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node2vec: Scalable feature learning for networks
Grover, A. & Leskovec, J · 2016
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Variational graph auto-encoders
Kipf, T. N. & Welling, M · 2016
Cited alongside, same era.
Network propagation: a universal amplifier of genetic associations
Cowen, L., Ideker, T., Raphael, B. J. & Sharan, R · 2017
Representation learning on graphs with jumping knowledge networks
Xu, K. et al · 2018
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Deepdta: deep drug–target binding affinity prediction
Öztürk, H., Özgür, A. & Ozkirimli, E · 2018
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Interpretable drug target prediction using deep neural representation
Gao, Y., Fokoue, A. et al · 2018
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Deep learning improves prediction of drug–drug and drug–food interactions
Ryu, J. Y., Kim, H. U. & Lee, S. Y · 2018
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Predicting drug-disease associations by using similarity constrained matrix factorization
Zhang, W. et al · 2018
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BioSNAP Datasets: Stanford biomedical network dataset collection (2018)
Zitnik, M., Sosič, R., Maheshwari, S. & Leskovec, J · 2018
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Cited alongside, same era.
A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information
Luo, Y. et al · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. & Welling, M · 2017
Cited alongside, same era.
struc2vec: Learning node representations from structural identity
Ribeiro, L. F., Saverese, P. H. & Figueiredo, D. R · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
Hamilton, W., Ying, Z. & Leskovec, J · 2017
Cited alongside, same era.
Computational prediction of drug-drug interactions based on drugs functional similarities
Ferdousi, R., Safdari, R. & Omidi, Y · 2017
Cited alongside, same era.
Predicting multicellular function through multi-layer tissue networks
Zitnik, M. & Leskovec, J · 2017
Cited alongside, same era.
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Link prediction based on graph neural networks
Zhang, M. & Chen, Y · 2018
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Clozapine use in geriatric patients—challenges
Mukku, S. S. R., Sivakumar, P. & Varghese, M · 2018
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Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities
Zitnik, M. et al · 2019
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A comprehensive survey on graph neural networks
Wu, Z. et al · 2019
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Mixhop: Higher-order graph convolution architectures via sparsified neighborhood mixing
Abu-El-Haija, S. et al · 2019
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Evolution of resilience in protein interactomes across the tree of life
Zitnik, M., Feldman, M. W., Leskovec, J. et al · 2019
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Network-based prediction of protein interactions
Kovács, I. A. et al · 2019
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Network embedding via coupled kernelized multi-dimensional array factorization
Xu, L., Cao, J., Wei, X. & Yu, P · 2019
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Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences
Tsubaki, M., Tomii, K. & Sese, J · 2019
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A reference map of the human protein interactome
Luck, K. et al · 2019
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The DisGeNET knowledge platform for disease genomics: 2019 update
Piñero, J. et al · 2019
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A reference map of the human binary protein interactome
Luck, K. et al · 2020
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Caster: Predicting drug interactions with chemical substructure representation
Huang, K., Xiao, C., Hoang, T. N., Glass, L. M. & Sun, J · 2020
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A comprehensive survey on geometric deep learning
Cao, W., Yan, Z., He, Z. & He, Z · 2020
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