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Updated: Jul 9, 2026

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使用注意力和稀疏的字典学习,对单细胞扰动数据进行可解释的建模

Yang Xu1, Stephen Fleming1, Matthew Tegtmeyer2

  • 1Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Cell systems
|April 5, 2025
PubMed
概括

一个新的深度学习模型CellCap分析单细胞扰动数据,揭示不同细胞状态如何独特地对遗传或化合物变化做出反应. 这种方法通过检查单个细胞的行为来揭示隐藏的生物学见解.

关键词:
贝叶斯字典学习学习贝叶斯字典学习深度生成模型深度生成模型可以解释的机器学习扰动分析是一种干扰分析.单细胞转录组学 单细胞转录组学

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...