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SPARSE: a sparse hypergraph neural network for learning multiple types of latent combinations to accurately predict drug–drug interactions

Nguyen, Duc Anh Nguyen, Canh Hao Petschner, Peter Mamitsuka, Hiroshi 京都大学 DOI:10.1093/bioinformatics/btac250

2022.07

概要

[Motivation] Predicting side effects of drug–drug interactions (DDIs) is an important task in pharmacology. The state-of-the-art methods for DDI prediction use hypergraph neural networks to learn latent representations of drugs and side effects to express high-order relationships among two interacting drugs and a side effect. The idea of these methods is that each side effect is caused by a unique combination of latent features of the corresponding interacting drugs. However, in reality, a side effect might have multiple, different mechanisms that cannot be represented by a single combination of latent features of drugs. Moreover, DDI data are sparse, suggesting that using a sparsity regularization would help to learn better latent representations to improve prediction performances. [Results] We propose SPARSE, which encodes the DDI hypergraph and drug features to latent spaces to learn multiple types of combinations of latent features of drugs and side effects, controlling the model sparsity by a sparse prior. Our extensive experiments using both synthetic and three real-world DDI datasets showed the clear predictive performance advantage of SPARSE over cutting-edge competing methods. Also, latent feature analysis over unknown top predictions by SPARSE demonstrated the interpretability advantage contributed by the model sparsity. [Availability and implementation] Code and data can be accessed at https://github.com/anhnda/SPARSE. [Supplementary information] Supplementary data are available at Bioinformatics online.

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参考文献

Anandkumar,A. et al. (2014) A tensor approach to learning mixed member- ship community models. J Mach Learn Res., 1, 2239–2312.

Baldo,B.A. (2018) Opioid analgesic drugs and serotonin toxicity (syndrome): mechanisms, animal models, and links to clinical effects. Arch. Toxicol., 92, 2457–2473.

Carvalho,C.M. et al. (2009) Handling sparsity via the horseshoe. In: Artificial Intelligence and Statistics, PMLR, Florida USA. pp. 73–80.

Dodick,D.W. et al. (2004) Cardiovascular tolerability and safety of triptans: a review of clinical data. Headache, 44, S20–S30.

Downs,A.M. et al. (2019) Trihexyphenidyl rescues the deficit in dopamine neurotransmission in a mouse model of DYT1 dystonia. Neurobiol. Dis., 125, 115–122.

Drugs.com (2021) Drug Interactions Checker. https://www.drugs.com/dru- g_interactions.html (25 December 2021, date last accessed).

Fagiolini,M. et al. (2004) Specific GABAA circuits for visual cortical plasticity. Science, 303, 1681–1683.

Fan,H. et al. (2021) Heterogeneous hypergraph variational autoencoder for link prediction. IEEE Trans. Pattern Anal. Mach. Intell., 1. https://doi.org/10.1109/TPAMI.2021.3059313.

Feng,Y. et al. (2019) Hypergraph neural networks. AAAI, 33, 3558–3565. Harada,S. et al. (2020) Dual graph convolutional neural network for predict- ing chemical networks. BMC Bioinformatics, 21, 1–13.

Hastie,T. (2015) Statistical Learning with Sparsity:The Lasso and Generalizations. Chapman and Hall/CRC Monographs on Statistics and Applied Probability. Vol. 143. CRC Press, Boca Raton, FL.

Hoeting,J.A. et al. (1999) Bayesian model averaging: a tutorial (with com- ments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors. Statist. Sci., 14, 382–417.

Kastrin,A. et al. (2018) Predicting potential drug-drug interactions on topo- logical and semantic similarity features using statistical learning. PLoS One, 13, e0196865.

Kipf,T.N. and Welling,M. (2016) Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, Conference Track Proceeding, OpenReview, Toulon, France, pp. 1–14. https://openreview.net/forum?id¼SJU4ayYgl.

Magro,L. et al. (2012) Epidemiology and characteristics of adverse drug reactions caused by drug–drug interactions. Expert Opin. Drug Saf., 11, 83–94.

Mei,S. and Zhang,K. (2021) A machine learning framework for predicting drug–drug interactions. Sci. Rep., 11, 17619.

Nguyen,D.A. et al. (2021) CentSmoothie: central-smoothing hypergraph neural net- works for predicting drug-drug interactions. arXiv, preprint arXiv:2112.07837. Cornell University, New York, USA. https://doi.org/10.48550/arXiv.2112.07837. Pal,S. and Zhu,Y. (2021) Community detection in the sparse hypergraph sto-chastic block model. Random Struct. Alg., 59, 407–463.

Piironen,J. and Vehtari,A. (2017) Sparsity information and regularization in the horseshoe and other shrinkage priors. Electron. J. Stat., 11, 5018–5051.

Rho,J.M. et al. (1997) Barbiturate-like actions of the propanediol dicarbamates felbamate and meprobamate. J. Pharmacol. Exp. Ther., 280, 1383–1391.

Ritter,J. et al. (2019) Rang and Dale’s Pharmacology. Elsevier, Amsterdam, Netherlands.

Saito,T. and Rehmsmeier,M. (2015) The precision-recall plot is more inform- ative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One, 10, e0118432.

Suleyman,H. et al. (2010) Different mechanisms in formation and prevention of indomethacin-induced gastric ulcers. Inflammation, 33, 224–234.

Tatonetti,N.P. et al. (2012) Data-driven prediction of drug effects and interac- tions. Sci. Transl. Med., 4, 125ra31.

Thelwall,M. et al. (2017) Is medical research informing professional practice more highly cited? Evidence from AHFS DI Essentials in drugs. com. Scientometrics, 112, 509–527.

Tibshirani,R. (1996) Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Series B Methodol., 58, 267–288.

Venkataraman,H. et al. (2014) Cytochrome P450-mediated bioactivation of mefenamic acid to quinoneimine intermediates and inactivation by human glutathione S-transferases. Chem. Res. Toxicol., 27, 2071–2081.

Wang,C.-S. et al. (2019) Detecting potential adverse drug reactions using a deep neural network model. J. Med. Internet Res., 21, e11016.

Xu,N. et al. (2019) MR-GNN: multi-resolution and dual graph neural net- work for predicting structured entity interactions. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence Organization, Macao, China. pp. 2628–2634.

Yadati,N. (2020) Neural message passing for multi-relational ordered and re- cursive hypergraphs. In: Advances in Neural Information Processing Systems. Vol. 33, Morgan Kaufmann Publishers Inc., Massachusetts, United States, pp. 3275–3289.

Zhang,Y. et al. (2019) Node features adjusted stochastic block model. J. Comput. Graph. Stat., 28, 362–373.

Zitnik,M. et al. (2018) Modeling polypharmacy side effects with graph convo- lutional networks. Bioinformatics, 34, i457–i466.

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