HyGraphRx: CONSTRUCTING HETEROGENEOUS GRAPHS USING HYBRID OPTIMIZATION FOR DRUG RECOMMENDATION

Authors

  • Swathi Mirthika G L,Dr. B.Sivakumar,Dr. Siddapuram ARVIND

Keywords:

Heterogeneous graphs, drug recommendation, hybrid optimization, genetic algorithms, graph neural networks, biomedical informatics, personalized medicine, PyTorch Geometric, knowledge graphs, machine learning

Abstract

The rapid expansion of biomedical data and the growing demand for personalized healthcare haveunderscored the need for intelligent drug recommendation systems that can effectively integrateand analyze heterogeneous information. This thesis introduces HyGraphRx, a comprehensiveframework designed to construct heterogeneous biomedical graphs and apply hybrid optimizationtechniques to enhance the accuracy and reliability of drug recommendations.

References

Zitnik, M., Agrawal, M., & Leskovec, J. (2023). Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 34(13), i457-i466. Available at: https://academic.oup.com/bioinformatics

Li, M. M., Huang, K., & Zitnik, M. (2022). Graph representation learning in biomedicine and healthcare. Nature Biomedical Engineering, 6(12), 1353-1369. Available at: https://www.nature.com/articles/s41551-022-00942-x

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Published

2024-05-15

How to Cite

Swathi Mirthika G L,Dr. B.Sivakumar,Dr. Siddapuram ARVIND. (2024). HyGraphRx: CONSTRUCTING HETEROGENEOUS GRAPHS USING HYBRID OPTIMIZATION FOR DRUG RECOMMENDATION. Journal of Computational Analysis and Applications (JoCAAA), 33(05), 1982–1997. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3155

Issue

Vol. 33 No. 05 (2024): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.