研究人员开发了一个名为Unbalanced Schrödinger Bridge (USB) 的新无模拟框架，用于从单细胞数据重建离散分支动力学。该方法通过在单细胞水平上准确捕捉细胞增殖和凋亡等生灭事件，解决了现有连续流体模型的局限性。USB整合了随机性和不平衡效应，提供了细胞谱系分支和命运决定的微观解释，并在模拟和真实世界数据集上均表现出强大的性能。 AI
影响 能够更准确地模拟细胞谱系和命运决定，从而改进生物学研究。
排序理由 详细介绍用于生物数据分析的新计算框架的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2605.00545v1 Announce Type: new Abstract: Inferring cellular trajectories from destructive snapshots is complicated by the challenges of stochasticity and non-conservative mass dynamics such as cell proliferation and apoptosis. Existing unbalanced Optimal Transport (OT) met…
Inferring cellular trajectories from destructive snapshots is complicated by the challenges of stochasticity and non-conservative mass dynamics such as cell proliferation and apoptosis. Existing unbalanced Optimal Transport (OT) methods treat mass as a continuous fluid, performin…