Cargando…
Learning from low-rank multimodal representations for predicting disease-drug associations
BACKGROUND: Disease-drug associations provide essential information for drug discovery and disease treatment. Many disease-drug associations remain unobserved or unknown, and trials to confirm these associations are time-consuming and expensive. To better understand and explore these valuable associ...
Autores principales: | Hu, Pengwei, Huang, Yu-an, Mei, Jing, Leung, Henry, Chen, Zhan-heng, Kuang, Ze-min, You, Zhu-hong, Hu, Lun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8567544/ https://www.ncbi.nlm.nih.gov/pubmed/34736437 http://dx.doi.org/10.1186/s12911-021-01648-x |
Ejemplares similares
-
High-Order Laplacian Regularized Low-Rank Representation for Multimodal Dementia Diagnosis
por: Dong, Aimei, et al.
Publicado: (2021) -
Multimodal Medical Image Fusion Based on Multiple Latent Low-Rank Representation
por: Lou, Xi-Cheng, et al.
Publicado: (2021) -
A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning
por: Zhao, Bo-Wei, et al.
Publicado: (2021) -
Brain Lesion Segmentation Based on Joint Constraints of Low-Rank Representation and Sparse Representation
por: Ge, Ting, et al.
Publicado: (2019) -
Low-Rank and Eigenface Based Sparse Representation for Face Recognition
por: Hou, Yi-Fu, et al.
Publicado: (2014)