Prediction of the Side Effects Associated with the Drug-Drug Interaction in Human Beings using Chaotic Particle Swarm Optimisation Based Deep Radial Networks
Keywords:
Prediction, side effects, drug-drug interaction, Chaotic Particle swarm optimisation, deep radial networksAbstract
Prediction of the side effects associated with the drug-drug interaction (DDI) in human beings using Chaotic Particle swarm optimisation (CPSO) based deep radial networks (DRN). As drug classes, feature vectors, pathways, target, and enzymes are utilised; afterwards, CPSO is utilised to extract feature interactions between these drug-related entities. We made use of DRN as a predictor of events associated with DDIs by basing it on the representation of characteristics. The findings indicate that when compared to several other metrics that are state-of-the-art, DRN-DDI performs better. In the meanwhile, we discuss the ways in which individual and combinational characteristics contribute. DRN-DDI provides greater advantages than other methods when it comes to the prediction of DDI events.
Downloads
References
Jang, H. Y., Song, J., Kim, J. H., Lee, H., Kim, I. W., Moon, B., & Oh, J. M. (2022). Machine learning-based quantitative prediction of drug exposure in drug-drug interactions using drug label information. npj Digital Medicine, 5(1), 88.
Hou, X., You, J., & Hu, P. (2019, February). Predicting drug-drug interactions using deep neural network. In Proceedings of the 2019 11th International Conference on Machine Learning and Computing (pp. 168-172).
Zhang, Y., Qiu, Y., Cui, Y., Liu, S., & Zhang, W. (2020). Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning. Methods, 179, 37-46.
Olayan, R. S., Ashoor, H., & Bajic, V. B. (2018). DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches. Bioinformatics, 34(7), 1164-1173.
Wang, Y., & Zeng, J. (2013). Predicting drug-target interactions using restricted Boltzmann machines. Bioinformatics, 29(13), i126-i134.
Chu, Y., Shan, X., Chen, T., Jiang, M., Wang, Y., Wang, Q., ... & Wei, D. Q. (2021). DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method. Briefings in bioinformatics, 22(3), bbaa205.
Shi, H., Liu, S., Chen, J., Li, X., Ma, Q., & Yu, B. (2019). Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure. Genomics, 111(6), 1839-1852.
Fu, G., Ding, Y., Seal, A., Chen, B., Sun, Y., & Bolton, E. (2016). Predicting drug target interactions using meta-path-based semantic network analysis. BMC bioinformatics, 17(1), 1-10.
Tanvir, F., Islam, M. I. K., & Akbas, E. (2021, October). Predicting drug-drug interactions using meta-path based similarities. In 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp. 1-8). IEEE.
Vilar, S., Harpaz, R., Uriarte, E., Santana, L., Rabadan, R., & Friedman, C. (2012). Drug—drug interaction through molecular structure similarity analysis. Journal of the American Medical Informatics Association, 19(6), 1066-1074.
Vilar, S., Uriarte, E., Santana, L., Friedman, C., & P Tatonetti, N. (2014). State of the art and development of a drug-drug interaction large scale predictor based on 3D pharmacophoric similarity. Current Drug Metabolism, 15(5), 490-501.
Luo, H., Zhang, P., Huang, H., Huang, J., Kao, E., Shi, L., ... & Yang, L. (2014). DDI-CPI, a server that predicts drug-drug interactions through implementing the chemical-protein interactome. Nucleic acids research, 42(W1), W46-W52.
Zakharov, A. V., Varlamova, E. V., Lagunin, A. A., Dmitriev, A. V., Muratov, E. N., Fourches, D., ... & Nicklaus, M. C. (2016). Qsar modeling and prediction of drug-drug interactions. Molecular pharmaceutics, 13(2), 545-556.
Huang, H., Zhang, P., Qu, X. A., Sanseau, P., & Yang, L. (2014). Systematic prediction of drug combinations based on clinical side-effects. Scientific reports, 4(1), 7160.
Herrero-Zazo, M., Segura-Bedmar, I., Hastings, J., & Martinez, P. (2015). DINTO: using OWL ontologies and SWRL rules to infer drug-drug interactions and their mechanisms. Journal of chemical information and modeling, 55(8), 1698-1707.
Cami, A., Manzi, S., Arnold, A., & Reis, B. Y. (2013). Pharmacointeraction network models predict unknown drug-drug interactions. PloS one, 8(4), e61468.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
