Co-contrastive Self-supervised Learning for Drug-Disease Association Prediction (original) (raw)
References
Bleakley, K., Yamanishi, Y.: Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics 25(18), 2397–2403 (2009) Article Google Scholar
Gottlieb, A., Stein, G.Y., Ruppin, E., Sharan, R.: Predict: a method for inferring novel drug indications with application to personalized medicine. Mol. Syst. Biol. 7(1), 496 (2011) Article Google Scholar
Öztürk, H., Özgür, A., Ozkirimli, E.: Deepdta: deep drug-target binding affinity prediction. Bioinformatics 34(17), i821–i829 (2018) Article Google Scholar
Zeng, X., Zhu, S., Liu, X., Zhou, Y., Nussinov, R., Cheng, F.: Deepdr: a network-based deep learning approach to in silico drug repositioning. Bioinformatics 35(24), 5191–5198 (2019) Article Google Scholar
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR. OpenReview.net, Toulon, France (2017) Google Scholar
Li, J., Zhang, S., Liu, T., Ning, C., Zhang, Z., Zhou, W.: Neural inductive matrix completion with graph convolutional networks for mirna-disease association prediction. Bioinformatics 36(8), 2538–2546 (2020) Article Google Scholar
Yu, Z., Huang, F., Zhao, X., Xiao, W., Zhang, W.: Predicting drug-disease associations through layer attention graph convolutional network. Briefings in Bioinformatics 22(4), bbaa243 (2021) Google Scholar
Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR. pp. 3733–3742. Computer Vision Foundation / IEEE Computer Society, Salt Lake City, UT, USA (2018) Google Scholar
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.E.: A simple framework for contrastive learning of visual representations. In: Proceedings of the 37th International Conference on Machine Learning, ICML. Proceedings of Machine Learning Research, vol. 119, pp. 1597–1607. PMLR, Virtual Event (2020) Google Scholar
Devries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. CoRR (2017). http://arxiv.org/abs/1708.04552
Howard, A.G.: Some improvements on deep convolutional neural network based image classification. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014 (2014) Google Scholar
Szegedy, C., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1–9. IEEE Computer Society (2015) Google Scholar
Zhao, C., Liu, S., Huang, F., Liu, S., Zhang, W.: Csgnn: contrastive self-supervised graph neural network for molecular interaction prediction. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI, pp. 3756–3763. IJCAI.org (2021) Google Scholar
Huang, C., et al.: Graph-enhanced multi-task learning of multi-level transition dynamics for session-based recommendation. In: 35th AAAI Conference on Artificial Intelligence, AAAI, pp. 4123–4130. AAAI Press, Virtual Event (2021) Google Scholar
Tong, H., Faloutsos, C., Pan, J.: Fast random walk with restart and its applications. In: Proceedings of the 6th IEEE International Conference on Data Mining ICDM, pp. 613–622. IEEE Computer Society, Hong Kong, China (2006) Google Scholar
Zhu, H., et al.: Bilinear graph neural network with neighbor interactions. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI, pp. 1452–1458. IJCAI.org (2020) Google Scholar
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: 3rd International Conference on Learning Representations, ICLR, San Diego, CA, USA, (2015) Google Scholar
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., Shen, Y.: Graph contrastive learning with augmentations. In: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems, NeurIPS, vol. 33, pp. 5812–5823 (2020) Google Scholar
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR (2015) Google Scholar
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, AISTATS, pp. 249–256. JMLR.org, Chia Laguna Resort, Sardinia, Italy (2010) Google Scholar
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014) MathSciNetMATH Google Scholar
Luo, H., et al.: Drug repositioning based on comprehensive similarity measures and bi-random walk algorithm. Bioinformatics 32(17), 2664–2671 (2016) Article Google Scholar
Liang, X., et al.: Lrssl: predict and interpret drug-disease associations based on data integration using sparse subspace learning. Bioinformatics 33(8), 1187–1196 (2017) Google Scholar
Zhang, W., et al.: Predicting drug-disease associations by using similarity constrained matrix factorization. BMC Bioinf. 19(1), 1–12 (2018) Article Google Scholar
Yang, M., Luo, H., Li, Y., Wang, J.: Drug repositioning based on bounded nuclear norm regularization. Bioinformatics 35(14), i455–i463 (2019) Article Google Scholar
Zhang, W., Xu, H., Li, X., Gao, Q., Wang, L.: Drimc: an improved drug repositioning approach using bayesian inductive matrix completion. Bioinformatics 36(9), 2839–2847 (2020) Article Google Scholar
Meng, Y., Lu, C., Jin, M., Xu, J., Zeng, X., Yang, J.: A weighted bilinear neural collaborative filtering approach for drug repositioning. Briefings in Bioinformatics 23(2), bbab581 (2022) Google Scholar