OtherPerspective
Deep-learning methods for unveiling large-scale single-cell transcriptomes
Xilin Shen and Xiangchun Li
Cancer Biology & Medicine December 2023, 20 (12) 972-980; DOI: https://doi.org/10.20892/j.issn.2095-3941.2023.0436
Xilin Shen
Tianjin Cancer Institute, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin Medical University, Tianjin 300060, China
Xiangchun Li
Tianjin Cancer Institute, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin Medical University, Tianjin 300060, China
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In this issue
Deep-learning methods for unveiling large-scale single-cell transcriptomes
Xilin Shen, Xiangchun Li
Cancer Biology & Medicine Dec 2023, 20 (12) 972-980; DOI: 10.20892/j.issn.2095-3941.2023.0436
Jump to section
- Article
- An overview of methods for analysing single-cell transcriptomes
- Deep learning-based methods for large-scale single-cell transcriptomes
- Prerequisites and challenges of deep-learning methods
- Advancing single-cell biology research with deep learning in the future
- Conflict of interest statement
- Author contributions
- References
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