Varyasyonel Çizge Otokodlayıcı Kullanarak Nanoparçacık, İlaç ve Kanser Arasındaki İlişkilerin Tahminlenmesi

Öz

Örtük ilaç-hastalık ilişkilerini tahmin etmek, yan etkileri ve geliştirme maliyetlerini en aza indirmek amacıyla yeni ilaçların geliştirilmesi için kritik öneme sahiptir. Var olan ilaç-hastalık tahmin yöntemleri tipik olarak ya tekli ya da çoklu ilaç-hastalık ağlarına odaklanmaktadır. Özellikle kanser araştırmalarında nanoparçacıklardaki son gelişmeler, toksik yan etkileri azaltarak biyoyararlanım ve farmakokinetikte gelişmeler göstermektedir. Bu nedenle, nanopartiküllerin ilaçlar ve hastalıklarla etkileşimi geliştirme aşamasında iyileşme eğilimindedir. Bu çalışmada, hedeflenen ilaç dağıtımı için bir bilgi tabanı olarak nanopartikül, ilaç ve kanser türleri arasındaki sınıf etkileşimlerini içeren hücreye özgü ilaç dağıtım verilerine varyasyonel bir çizge otokodlayıcı modeli sunmaktadır. Hücreye özgü ilaç verme verileri, ilişkilerin yalnızca bu sınıf etkileşimlerinin dizileri arasında var olduğu iki parçalı bir grafiğe dönüştürülür. Deneysel sonuçlar, bilgi çizgesi ile geliştirilmiş Varyasyonel Çizge Otokodlayıcı modelinin VGAE-ROC-AUC (0.9627) ve VGAE-AP (0.9566) skorlarıyla Çizge Otokodlayıcı modelinden daha iyi performans sergilediğini göstermektedir.

Anahtar Kelimeler

• Varyasyonel Çizge Otokodlayıcı
• Nanoparçacıklar
• İlaç-hastalık İlişkisi

Prediction of Associations between Nanoparticle, Drug and Cancer Using Variational Graph Autoencoder

Öz

Predicting implicit drug-disease associations is critical to the development of new drugs, with the aim of minimizing side effects and development costs. Existing drug-disease prediction methods typically focus on either single or multiple drug-disease networks. Recent advances in nanoparticles particularly in cancer research show improvements in bioavailability and pharmacokinetics by reducing toxic side effects. Thus, the interaction of the nanoparticles with drugs and diseases tends to improve during the development phase. In this study, it presents a variational graph autoencoder model to the cell-specific drug delivery data, including the class interactions between nanoparticle, drug, and cancer types as a knowledge base for targeted drug delivery. The cell-specific drug delivery data is transformed into a bipartite graph where relations only exist between sequences of these class interactions. Experimental results show that the knowledge graph enhanced Variational Graph Autoencoder model with VGAE-ROC-AUC (0.9627) and VGAE-AP (0.9566) scores performs better than the Graph Autoencoder model.

Anahtar Kelimeler

• Variational Graph Autoencoder
• Nanoparticles
• Drug–disease Association

Kaynakça

1. Liu, Y., Yang, G., Jin, S., Xu, L., & Zhao, C. X. 2020. Development of high‐drug‐loading nanoparticles. ChemPlusChem, 85(9), 2143-2157.
2. Sozer, S. C., Ozmen Egesoy, T., Basol, M., Cakan-Akdogan, G., Akdogan, Y. 2020. A simple desolvation method for production of cationic albumin nanoparticles with improved drug loading and cell uptake. Journal of Drug Delivery Science and Technology. Volume 60, 101931, ISSN 1773-2247. https://doi.org/10.1016/j.jddst.2020.101931.
3. Akdogan, Y., Sozer, S. C., Akyol, C., Basol, M., Karakoyun, C., Cakan-Akdogan, G. 2022. Synthesis of albumin nanoparticles in a water-miscible ionic liquid system, and their applications for chlorambucil delivery to cancer cells. Journal of Molecular Liquids. Volume 367, Part B, 120575, ISSN0167-7322. https://doi.org/10.1016/j.molliq.2022.120575.
4. Rubin, D. L., Lewis, S. E., Mungall, C. J., Misra, S., Westerfield, M., Ashburner, M., ... & Musen, M. A. 2006. National center for biomedical ontology: advancing biomedicine through structured organization of scientific knowledge. Omics: a journal of integrative biology, 10(2), 185-198.
5. Lever, J., Zhao, E. Y., Grewal, J., Jones, M. R., & Jones, S. J. 2019. CancerMine: a literature-mined resource for drivers, oncogenes and tumor suppressors in cancer. Nature methods, 16(6), 505-507.
6. Launer-Wachs, S., Taub-Tabib, H., Goldberg, Y., & Shamay, Y. 2022. Rapid Knowledgebase Construction and Hypotheses Generation Using Extractive Literature Search. bioRxiv, 2022-02.
7. Gottlieb, A., Stein, G. Y., Ruppin, E., & Sharan, R. 2011. PREDICT: a method for inferring novel drug indications with application to personalized medicine. Molecular systems biology, 7(1), 496.
8. Luo, H., Li, M., Wang, S., Liu, Q., Li, Y., & Wang, J. 2018. Computational drug repositioning using low-rank matrix approximation and randomized algorithms. Bioinformatics, 34(11), 1904-1912.

9. Zhang, W., Yue, X., Lin, W., Wu, W., Liu, R., Huang, F., & Liu, F. 2018. Predicting drug-disease associations by using similarity constrained matrix factorization. BMC bioinformatics, 19, 1-12.
10. Wang, M. N., You, Z. H., Li, L. P., Chen, Z. H., & Xie, X. J. 2020. WGMFDDA: A novel weighted-based graph regularized matrix factorization for predicting drug-disease associations. In Intelligent Computing Methodologies: 16th International Conference, ICIC 2020, Bari, Italy, October 2–5, 2020, Proceedings, Part III 16. Springer International Publishing, 542-551.
11. Liang, X., Zhang, P., Yan, L., Fu, Y., Peng, F., Qu, L., ... & Chen, Z. 2017. LRSSL: predict and interpret drug–disease associations based on data integration using sparse subspace learning. Bioinformatics, 33(8), 1187-1196.
12. Zhang, W., Yue, X., Chen, Y., Lin, W., Li, B., Liu, F., & Li, X. 2017. Predicting drug-disease associations based on the known association bipartite network. In 2017 IEEE international conference on bioinformatics and biomedicine (BIBM) IEEE, 503-509.
13. Wang, B., Lyu, X., Qu, J., Sun, H., Pan, Z., & Tang, Z. 2019. GNDD: a graph neural network-based method for drug-disease association prediction. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) IEEE, 1253-1255.
14. Yu, Z., Huang, F., Zhao, X., Xiao, W., & Zhang, W. 2021. Predicting drug–disease associations through layer attention graph convolutional network, Briefings in Bioinformatics, 22(4), bbaa243.
15. Kipf, T. N., & Welling, M. 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308.
16. Taub-Tabib, H., Shlain, M., Sadde, S., Lahav, D., Eyal, M., Cohen, Y., & Goldberg, Y. 2020. Interactive Extractive Search over Biomedical Corpora. In Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing, 28-37.
17. Fadilah, N. I. M., Isa, I. L. M., Zaman, W. S. W. K., Tabata, Y., & Fauzi, M. B. 2022. The effect of nanoparticle-incorporated natural-based biomaterials towards cells on activated pathways: a systematic review. Polymers, 14(3), 476.
18. Joshy, K. S., Susan, M. A., Snigdha, S., Nandakumar, K., Laly, A. P., & Sabu, T. 2018. Encapsulation of zidovudine in PF-68 coated alginate conjugate nanoparticles for anti-HIV drug delivery. International journal of biological macromolecules, 107, 929-937.
19. Wishart, D. S., Knox, C., Guo, A. C., Shrivastava, S., Hassanali, M., Stothard, P., ... & Woolsey, J. 2006. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic acids research, 34(suppl_1), D668-D672.
20. Uhlen, M., Zhang, C., Lee, S., Sjöstedt, E., Fagerberg, L., Bidkhori, G., ... & Ponten, F. 2017. A pathology atlas of the human cancer transcriptome. Science, 357(6352), eaan2507.
21. Canese, K., & Weis, S. 2013. PubMed: the bibliographic database. The NCBI handbook, 2(1).
22. Kipf, T.N., Welling, M. 2017. Semi-Supervised Classification with Graph Convolutional Networks. 5th International Conference on Learning Representations, ICLR. Toulon, France, April 24-26, Conference Track Proceedings.
23. Fey, M., & Lenssen, J. E. 2019. Fast graph representation learning with PyTorch Geometric. arXiv preprint arXiv:1903.02428.
24. Le, Q., & Mikolov, T. 2014. Distributed representations of sentences and documents. In International conference on machine learning (pp. 1188-1196). PMLR.
25. Reimers, N., & Gurevych, I. (2019, November). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 3982-3992.