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DropDAE:在scRNA-seq数据中使用对比学习进行自编码

Wanlin Juan1, Kwang Woo Ahn1, Yi-Guang Chen2

  • 1Division of Biostatistics, Data Science Institute, Medical College of Wisconsin (MCW), Milwaukee, WI 53226, USA.

Bioengineering (Basel, Switzerland)
|August 28, 2025
PubMed
概括

一个新的深度学习模型DropDAE有效地解决了单细胞RNA测序数据中的脱落事件. 这种方法改善了基因表达数据的重建,并提高了细胞聚类的准确性和稳定性.

关键词:
自动编码器深度学习无声自动编码器退学情况归算方式scRNA-seq 在

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