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scPheno: A deep generative model to integrate scRNA-seq with disease phenotypes and its application on prediction of COVID-19 pneumonia and severe assessment

Feng Zeng, Xuwen Kong, Fan Yang, Ting Chen, Jiahuai Han
doi: https://doi.org/10.1101/2022.06.20.496916
Feng Zeng
1Department of Automation, Xiamen University, Xiamen, Fujian 361102, China
2Department of Neuroscience, School of Medicine, Xiamen University, Xiamen, Fujian 361102, China
3Department of Neurosurgery, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, Fujian, China
5National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
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  • For correspondence: zengfeng@xmu.edu.cn jhan@xmu.edu.cn
Xuwen Kong
1Department of Automation, Xiamen University, Xiamen, Fujian 361102, China
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Fan Yang
1Department of Automation, Xiamen University, Xiamen, Fujian 361102, China
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Ting Chen
6Institute for Artificial Intelligence, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
7Tsinghua-Fuzhou Institute of Digital Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
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Jiahuai Han
4State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen 361005, China
5National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
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  • For correspondence: zengfeng@xmu.edu.cn jhan@xmu.edu.cn
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Posted June 21, 2022.
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scPheno: A deep generative model to integrate scRNA-seq with disease phenotypes and its application on prediction of COVID-19 pneumonia and severe assessment
Feng Zeng, Xuwen Kong, Fan Yang, Ting Chen, Jiahuai Han
bioRxiv 2022.06.20.496916; doi: https://doi.org/10.1101/2022.06.20.496916
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scPheno: A deep generative model to integrate scRNA-seq with disease phenotypes and its application on prediction of COVID-19 pneumonia and severe assessment
Feng Zeng, Xuwen Kong, Fan Yang, Ting Chen, Jiahuai Han
bioRxiv 2022.06.20.496916; doi: https://doi.org/10.1101/2022.06.20.496916

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