Cargando…
MGAE-DC: Predicting the synergistic effects of drug combinations through multi-channel graph autoencoders
Accurate prediction of synergistic effects of drug combinations can reduce the experimental costs for drug development and facilitate the discovery of novel efficacious combination therapies for clinical studies. The drug combinations with high synergy scores are regarded as synergistic ones, while...
Autores principales: | Zhang, Peng, Tu, Shikui |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10027223/ https://www.ncbi.nlm.nih.gov/pubmed/36867661 http://dx.doi.org/10.1371/journal.pcbi.1010951 |
Ejemplares similares
-
Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder
por: Chen, Peng, et al.
Publicado: (2023) -
Predicting Drug Synergy and Discovering New Drug Combinations Based on a Graph Autoencoder and Convolutional Neural Network
por: Li, Huijun, et al.
Publicado: (2023) -
Multi-task learning for predicting synergistic drug combinations based on auto-encoding multi-relational graphs
por: Shan, Wenyu, et al.
Publicado: (2023) -
VGAEDTI: drug-target interaction prediction based on variational inference and graph autoencoder
por: Zhang, Yuanyuan, et al.
Publicado: (2023) -
A complete graph-based approach with multi-task learning for predicting synergistic drug combinations
por: Wang, Xiaowen, et al.
Publicado: (2023)