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Predicting interactions among heterogenous graph structured data has numerous applications such as knowledge graph completion, recommendation systems and drug discovery.
Drug repositioning: identifying and developing new uses for existing drugs
Ashburn, T. T. and Thor, K. B · 2004
Earlier work this paper cites.
Graph theory enables drug repurposing–how a mathematical model can drive the discovery of hidden mechanisms of action
Gramatica, R., Di Matteo, T., Giorgetti, S., Barbiani, M., Bevec, D., and Aste, T · 2014
Earlier work this paper cites.
The mintact project—intact as a common curation platform for 11 molecular interaction databases
Orchard, S., Ammari, M., Aranda, B., Breuza, L., Briganti, L., Broackes-Carter, F., Campbell, N. H., Chavali, G., Chen, C., Del-Toro, N., et al · 2014
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Examination of clinical trial costs and barriers for drug development: report to the assistant secretary of planning and evaluation (aspe)
Sertkaya, A., Birkenbach, A., Berlind, A., and Eyraud, J · 2014
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Embedding entities and relations for learning and inference in knowledge bases
Yang, B., Yih, W.-t., He, X., Gao, J., and Deng, L · 2014
Earlier work this paper cites.
The $2.6 billion pill–methodologic and policy considerations
Avorn, J. et al · 2015
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Nffinder: an online bioinformatics tool for searching similar transcriptomics experiments in the context of drug repositioning
Setoain, J., Franch, M., Martínez, M., Tabas-Madrid, D., Sorzano, C. O., Bakker, A., Gonzalez-Couto, E., Elvira, J., and Pascual-Montano, A · 2015
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Complex embeddings for simple link prediction
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., and Bouchard, G · 2016
Earlier work this paper cites.
Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing
Udrescu, L., Sbârcea, L., Topîrceanu, A., Iovanovici, A., Kurunczi, L., Bogdan, P., and Udrescu, M · 2016
Cited alongside, same era.
DGIdb 3.0: a redesign and expansion of the drug–gene interaction database
Cotto, K. C., Wagner, A. H., Feng, Y.-Y., Kiwala, S., Coffman, A. C., Spies, G., Wollam, A., Spies, N. C., Griffith, O. L., and Griffith, M · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
Cited alongside, same era.
Drugbank 5.0: a major update to the drugbank database for 2018 nucleic acids res
DS, W., YD, F., AC, G., EJ, L., A, M., JR, G., T, S., D, J., C, L., Z, S., N, A., I, I., Y, L., A, M., N, G., A, W., L, C., R, C., D, L., A, P., C, K., and M, W · 2018
Cited alongside, same era.
String v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets
D, S., AL, G., D, L., A, J., S, W., J, H.-C., M, S., NT, D., JH, M., P, B., LJ, J., and von Mering C · 2019
Later among the works it cites.
Rotate: Knowledge graph embedding by relational rotation in complex space
Sun, Z., Deng, Z.-H., Nie, J.-Y., and Tang, J · 2019
Later among the works it cites.
Deep graph library: Towards efficient and scalable deep learning on graphs
Wang, M., Yu, L., Zheng, D., Gan, Q., Gai, Y., Ye, Z., Li, M., Zhou, J., Huang, Q., Ma, C., et al · 2019
Later among the works it cites.
Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding
Fu, X., Zhang, J., Meng, Z., and King, I · 2020
Closest in time.
A sars-cov-2-human protein-protein interaction map reveals drug targets and potential drug-repurposing
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A global network of biomedical relationships derived from text
Percha, B. and Altman, R. B · 2018
Cited alongside, same era.
Modeling relational data with graph convolutional networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., Van Den Berg, R., Titov, I., and Welling, M · 2018
Cited alongside, same era.
Meta relational learning for few-shot link prediction in knowledge graphs
Chen, M., Zhang, W., Zhang, W., Chen, Q., and Chen, H · 2019
Cited alongside, same era.
Systematic integration of biomedical knowledge prioritizes drugs for repurposing
DS, H., A, L., C, H., L, B., SL, C., D, H., A, G., P, K., and SE, B
Cited in the paper.
Knowledge graph embedding: A survey of approaches and applications
Wang, Q., Mao, Z., Wang, B., and Guo, L
Cited in the paper.
Accurate de novo prediction of protein contact map by ultra-deep learning model
Wang, S., Sun, S., Li, Z., Zhang, R., and Xu, J
Cited in the paper.
Gordon, D. E., Jang, G. M., Bouhaddou, M., Xu, J., Obernier, K., O’meara, M. J., Guo, J. Z., Swaney, D. L., Tummino, T. A., Huttenhain, R., et al · 2020
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Drkg - drug repurposing knowledge graph for covid-19
Ioannidis, V. N., Song, X., Manchanda, S., Li, M., Pan, X., Zheng, D., Ning, X., Zeng, X., and Karypis, G · 2020
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Dgl-ke: Training knowledge graph embeddings at scale
Zheng, D., Song, X., Ma, C., Tan, Z., Ye, Z., Dong, J., Xiong, H., Zhang, Z., and Karypis, G · 2020
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Network-based drug repurposing for novel coronavirus 2019-ncov/sars-cov-2
Zhou, Y., Hou, Y., Shen, J., Huang, Y., Martin, W., and Cheng, F · 2020
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