Cargando…
DDA-SKF: Predicting Drug–Disease Associations Using Similarity Kernel Fusion
Drug repositioning provides a promising and efficient strategy to discover potential associations between drugs and diseases. Many systematic computational drug-repositioning methods have been introduced, which are based on various similarities of drugs and diseases. In this work, we proposed a new...
Autores principales: | Gao, Chu-Qiao, Zhou, Yuan-Ke, Xin, Xiao-Hong, Min, Hui, Du, Pu-Feng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8792612/ https://www.ncbi.nlm.nih.gov/pubmed/35095495 http://dx.doi.org/10.3389/fphar.2021.784171 |
Ejemplares similares
-
LPI-SKF: Predicting lncRNA-Protein Interactions Using Similarity Kernel Fusions
por: Zhou, Yuan-Ke, et al.
Publicado: (2020) -
SKF-LDA: Similarity Kernel Fusion for Predicting lncRNA-Disease Association
por: Xie, Guobo, et al.
Publicado: (2019) -
MDA-SKF: Similarity Kernel Fusion for Accurately Discovering miRNA-Disease Association
por: Jiang, Limin, et al.
Publicado: (2018) -
SKF : catálogo general.
Publicado: (1989) -
Inhibitory effects of SKF96365 on the activities of K(+) channels in mouse small
intestinal smooth muscle cells
por: TANAHASHI, Yasuyuki, et al.
Publicado: (2015)