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...

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Autores principales:	Peilin Jia, Ruifeng Hu, Guangsheng Pei, Yulin Dai, Yin-Ying Wang, Zhongming Zhao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/4fb77b5018df4b45a1a0baa6a40058e4
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spelling	oai:doaj.org-article:4fb77b5018df4b45a1a0baa6a40058e42021-12-02T17:04:52ZDeep generative neural network for accurate drug response imputation10.1038/s41467-021-21997-52041-1723https://doaj.org/article/4fb77b5018df4b45a1a0baa6a40058e42021-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-21997-5https://doaj.org/toc/2041-1723Drug 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 and accurately impute drug response.Peilin JiaRuifeng HuGuangsheng PeiYulin DaiYin-Ying WangZhongming ZhaoNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Peilin Jia Ruifeng Hu Guangsheng Pei Yulin Dai Yin-Ying Wang Zhongming Zhao Deep generative neural network for accurate drug response imputation
description	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 and accurately impute drug response.
format	article
author	Peilin Jia Ruifeng Hu Guangsheng Pei Yulin Dai Yin-Ying Wang Zhongming Zhao
author_facet	Peilin Jia Ruifeng Hu Guangsheng Pei Yulin Dai Yin-Ying Wang Zhongming Zhao
author_sort	Peilin Jia
title	Deep generative neural network for accurate drug response imputation
title_short	Deep generative neural network for accurate drug response imputation
title_full	Deep generative neural network for accurate drug response imputation
title_fullStr	Deep generative neural network for accurate drug response imputation
title_full_unstemmed	Deep generative neural network for accurate drug response imputation
title_sort	deep generative neural network for accurate drug response imputation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4fb77b5018df4b45a1a0baa6a40058e4
work_keys_str_mv	AT peilinjia deepgenerativeneuralnetworkforaccuratedrugresponseimputation AT ruifenghu deepgenerativeneuralnetworkforaccuratedrugresponseimputation AT guangshengpei deepgenerativeneuralnetworkforaccuratedrugresponseimputation AT yulindai deepgenerativeneuralnetworkforaccuratedrugresponseimputation AT yinyingwang deepgenerativeneuralnetworkforaccuratedrugresponseimputation AT zhongmingzhao deepgenerativeneuralnetworkforaccuratedrugresponseimputation
_version_	1718381790430756864

Deep generative neural network for accurate drug response imputation

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