Deep generative neural network for accurate drug response imputation
Drug response in cancer patients vary dramatically due to inter- and intra-tumor heterogeneity and transcriptome context plays a significant role in shaping the actual treatment outcome. Here, the authors develop a deep variational autoencoder model to compress gene signatures into latent vectors an...
Guardado en:
Autores principales: | Peilin Jia, Ruifeng Hu, Guangsheng Pei, Yulin Dai, Yin-Ying Wang, Zhongming Zhao |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/4fb77b5018df4b45a1a0baa6a40058e4 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Surface protein imputation from single cell transcriptomes by deep neural networks
por: Zilu Zhou, et al.
Publicado: (2020) -
Accurate imputation of human leukocyte antigens with CookHLA
por: Seungho Cook, et al.
Publicado: (2021) -
Deep neural networks for accurate predictions of crystal stability
por: Weike Ye, et al.
Publicado: (2018) -
An accurate and robust imputation method scImpute for single-cell RNA-seq data
por: Wei Vivian Li, et al.
Publicado: (2018) -
Missing Value Imputation of Time-Series Air-Quality Data via Deep Neural Networks
por: Taesung Kim, et al.
Publicado: (2021)