Abstract
Traditional drug discovery is a costly and lengthy project. The computational drug repositioning methods can be used to quickly and systematically discover possible drug-disease relationships, help guide biological experiments, and speed up the drug discovery process. Transcriptomics data is widely employed in computational drug repositioning research, but current computational methods based on transcriptomics data still have some limitations. As a consequence of that, we implement a deep learning model BSageIMC based on drug/disease transcriptomics and PPI data. BSageIMC model uses bipartite graph convolutional neural networks, which can effectively fuse drugs, diseases and proteins information, and then learn the features of drugs and diseases. Then BSageIMC model reconstructs the drug-disease relationship matrix through the inductive matrix completion algorithm, and finally realizes accurate prediction of the drug-disease relationships. The comparison results with the three existing methods GCN, Node2Vec and deepDR and the case study of breast cancer demonstrate the reliability and predictive accuracy of our model, which shows that our model can be effectively used in drug repositioning research.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pulley, J., Rhoads, J.P., Jerome, R., et al.: Using what we already have: uncovering new drug repurposing strategies in existing omics data. Annu. Rev. Pharmacol. Toxicol. 60, 333–352 (2020)
Saberian, N., Peyvandi-Pour, A., Donato, M., et al.: A new computational drug repurposing method using established disease-drug pair knowledge. Bioinformatics 35(19), 3672–3678 (2019)
Aliper, A., Plis, S., Artemov, A.V., et al.: Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol. Pharm. 13(7), 2524–2530 (2016)
Peyvandi-Pour, A., Saberian, N., Shafi, A., et al.: A novel computational approach for drug repurposing using systems biology. Bioinformatics 34(16), 2817–2825 (2018)
Zhou, Y., Fang, J., Bekris, L., et al.: AlzGPS: a genome-wide positioning systems platform to catalyze multi-omics for Alzheimer’ s drug discovery. Alzheimer’s Res. Therapy 13(1), 24 (2021)
Li, Z., Shen, X., Jiao, Y., et al.: Hierarchical bipartite graph neural networks: towards largescale e-commerce applications. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 1677–1688. IEEE, Dallas (2020)
Li, J., Zhang, S., Liu, T., et al.: Neural inductive matrix completion with graph convolutional networks for mirna-disease association prediction. Bioinformatics 36(8), 2538–2546 (2020)
Wang, Z., Zhou, M., Arnold, C.R.: Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing. Bioinformatics 36(Supply_1), i525–i533 (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations (ICLR 2017), pp. 1–14. International Conference on Learning Representations, Toulon (2017)
Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. Association for Computing Machinery, New York (2016).
Zeng, X., Zhu, S., Liu, X., et al.: deepDR: a network-based deep learning approach to in silico drug repositioning. Bioinformatics 35(24), 5191–5198 (2019)
Acknowledgements
Our research was supported by the NSFC (Grant No. 41877305, 41705097).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, J., Lv, X., Jiang, S. (2022). BSageIMC: Drug Repositioning Based on Bipartite Graph Convolutional Networks and Transcriptomics Data. In: Li, X. (eds) Advances in Intelligent Automation and Soft Computing. IASC 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-81007-8_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-81007-8_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-81006-1
Online ISBN: 978-3-030-81007-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)