Cargando…
Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer’s disease drug repurposing
Predictive modeling of drug-induced gene expressions is a powerful tool for phenotype-based compound screening and drug repurposing. State-of-the-art machine learning methods use a small number of fixed cell lines as a surrogate for predicting actual expressions in a new cell type or tissue, althoug...
Autores principales: | Wu, You, Liu, Qiao, Qiu, Yue, Xie, Lei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9398009/ https://www.ncbi.nlm.nih.gov/pubmed/35951653 http://dx.doi.org/10.1371/journal.pcbi.1010367 |
Ejemplares similares
-
A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing
por: Pham, Thai-Hoang, et al.
Publicado: (2021) -
Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing
por: Pham, Thai-Hoang, et al.
Publicado: (2022) -
Repurposing of Tibolone in Alzheimer’s Disease
por: Barreto, George E.
Publicado: (2023) -
Quantitative and multiplexed chemical-genetic phenotyping in mammalian cells with QMAP-Seq
por: Brockway, Sonia, et al.
Publicado: (2020) -
In silico repurposing of antipsychotic drugs for Alzheimer’s disease
por: Kumar, Shivani, et al.
Publicado: (2017)