Prediction of Associations between Nanoparticle, Drug and Cancer Using Variational Graph Autoencoder
-
Published:2024-01-23
Issue:76
Volume:26
Page:167-172
-
ISSN:1302-9304
-
Container-title:Deu Muhendislik Fakultesi Fen ve Muhendislik
-
language:tr
-
Short-container-title:DEUFMD
Affiliation:
1. IZMIR INSTITUTE OF TECHNOLOGY
Abstract
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.
Publisher
Deu Muhendislik Fakultesi Fen ve Muhendislik
Reference25 articles.
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.
|
|