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English(EN) Predictive Multiplicity in Cell-Fate Assignment: Label-Free Rashomon Sets and the Limits of Per-Cell Certification

新框架解决单细胞分析中冲突的细胞命运分配问题

开发了一个名为FateMultiplicity的新框架,用于解决单细胞轨迹推断中冲突的细胞命运分配问题。这种无标签的方法通过在交叉拟合基因上评估模型差异来构建一组统计上可接受的模型,称为拉蒙塞特(Rashomon set),而无需依赖谱系标签。研究表明,模型的数量本身不如模型的多样性对多重性影响显著,并且与模型自身的置信度相比,单细胞认证并不能可靠地提高命运预测的准确性。 AI

影响 引入了一个新颖的计算框架用于分析生物数据,可能提高细胞命运预测的可靠性。

排序理由 在arXiv上发表的学术论文,详细介绍了用于生物数据分析的新计算框架。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新框架解决单细胞分析中冲突的细胞命运分配问题

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在arXiv上发表的学术论文,详细介绍了用于生物数据分析的新计算框架。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Arjun Bhupatiraju, Abhiram Bhupatiraju ·

    细胞命运分配中的预测性多重性:无标记Rashomon集合与单细胞认证的局限性

    arXiv:2610.11185v1 Announce Type: new Abstract: Single-cell trajectory inference maps transcriptomic measurements onto developmental continua, yet configurations that fit the data equally well can assign conflicting cell fates. FateMultiplicity is a label-free framework that cons…