Cargando…
SWnet: a deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures
BACKGROUND: One of the major challenges in precision medicine is accurate prediction of individual patient’s response to drugs. A great number of computational methods have been developed to predict compounds activity using genomic profiles or chemical structures, but more exploration is yet to be d...
Autores principales: | Zuo, Zhaorui, Wang, Penglei, Chen, Xiaowei, Tian, Li, Ge, Hui, Qian, Dahong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434731/ https://www.ncbi.nlm.nih.gov/pubmed/34507532 http://dx.doi.org/10.1186/s12859-021-04352-9 |
Ejemplares similares
-
An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification
por: Probst, Daniel
Publicado: (2023) -
Profiling and analysis of chemical compounds using pointwise mutual information
por: Čmelo, I., et al.
Publicado: (2021) -
Drug-target interaction prediction using semi-bipartite graph model and deep learning
por: Eslami Manoochehri, Hafez, et al.
Publicado: (2020) -
Deep phenotyping: deep learning for temporal phenotype/genotype classification
por: Taghavi Namin, Sarah, et al.
Publicado: (2018) -
Statistical-based database fingerprint: chemical space dependent representation of compound databases
por: Sánchez-Cruz, Norberto, et al.
Publicado: (2018)