Cargando…
Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data
Drug screening data from massive bulk gene expression databases can be analyzed to determine the optimal clinical application of cancer drugs. The growing amount of single-cell RNA sequencing (scRNA-seq) data also provides insights into improving therapeutic effectiveness by helping to study the het...
Autores principales: | Chen, Junyi, Wang, Xiaoying, Ma, Anjun, Wang, Qi-En, Liu, Bingqiang, Li, Lang, Xu, Dong, Ma, Qin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9618578/ https://www.ncbi.nlm.nih.gov/pubmed/36310235 http://dx.doi.org/10.1038/s41467-022-34277-7 |
Ejemplares similares
-
DESSO-DB: A web database for sequence and shape motif analyses and identification
por: Wang, Xiaoying, et al.
Publicado: (2022) -
IRIS3: integrated cell-type-specific regulon inference server from single-cell RNA-Seq
por: Ma, Anjun, et al.
Publicado: (2020) -
Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework
por: Yang, Jinyu, et al.
Publicado: (2019) -
Deep learning analysis of single‐cell data in empowering clinical implementation
por: Ma, Anjun, et al.
Publicado: (2022) -
MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer
por: Wang, Xiaoying, et al.
Publicado: (2023)