English(EN) From Snapshots to Trajectories: Learning Single-Cell Gene Expression Dynamics via Conditional Flow Matching

新的scFM方法模拟单细胞基因表达动力学

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-22 04:00

研究人员开发了一个名为单细胞流匹配（scFM）的新框架，以更好地模拟单细胞中基因表达的动力学。该方法解决了现有技术中的挑战，例如离散时间点之间转换的模糊性以及长期预测中的误差累积。通过使用条件流匹配和双向速度场，scFM提高了时间插值和外推的准确性，从而更忠实地重建基因表达动力学。 AI

影响引入了一种新颖的生成框架，用于分析复杂的生物时间序列数据，有望改善药物发现和疾病研究。

排序理由关于用于生物数据分析的新型机器学习方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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

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arXiv cs.LG TIER_1 English(EN) · Siyu Pu, Qingqing Long, Xiaohan Huang, Haotian Chen, Jiajia Wang, Meng Xiao, Xiao Luo, Hengshu Zhu, Yuanchun Zhou, Xuezhi Wang · 2026-05-22 04:00

从快照到轨迹：通过条件流匹配学习单细胞基因表达动力学

arXiv:2605.22340v1 Announce Type: new Abstract: Single-cell RNA sequencing (scRNA-seq) provides high-dimensional profiles of cellular states, enabling data-driven modeling of cellular dynamics over time. In practice, time-resolved scRNA-seq is collected at only a few discrete tim…