Cargando…
A complete graph-based approach with multi-task learning for predicting synergistic drug combinations
MOTIVATION: Drug combination therapy shows significant advantages over monotherapy in cancer treatment. Since the combinational space is difficult to be traversed experimentally, identifying novel synergistic drug combinations based on computational methods has become a powerful tool for pre-screeni...
Autores principales: | Wang, Xiaowen, Zhu, Hongming, Chen, Danyi, Yu, Yongsheng, Liu, Qi, Liu, Qin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256571/ https://www.ncbi.nlm.nih.gov/pubmed/37261842 http://dx.doi.org/10.1093/bioinformatics/btad351 |
Ejemplares similares
-
Predicting anticancer synergistic drug combinations based on multi-task learning
por: Chen, Danyi, 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) -
Multi-Task Learning and Improved TextRank for Knowledge Graph Completion
por: Tian, Hao, et al.
Publicado: (2022) -
Prediction of Synergistic Antibiotic Combinations by Graph Learning
por: Lv, Ji, et al.
Publicado: (2022) -
PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network
por: Wang, Xiaowen, et al.
Publicado: (2022)