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Updated: Jan 10, 2026

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
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scGALA推进了基于图形链接预测的单元对齐,以实现全面的数据集成和协调.

Guo Jiang1,2, Kailu Song2,3, Gregory J Fonseca4

  • 1Department of Medicine, Division of Experimental Medicine, McGill University, Montreal, QC, Canada.

Nature communications
|November 26, 2025
PubMed
概括

新的基于图形的学习框架scGALA通过改善细胞对齐来增强单细胞数据集成. 它在各种数据集中实现了更准确的细胞对应,促进了下游分析任务.

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