EPISODE · Jan 12, 2026 · 16 MIN
392-scDiffEq：利用神经随机微分方程学习细胞动力学
from 聊聊Sci
这项研究介绍了一种名为 scDiffEq 的创新生成式深度学习框架，旨在通过神经随机微分方程模拟单细胞生物学的动态演变。该模型超越了传统方法，能够同时捕捉细胞状态转换中的确定性漂移与随机扩散，从而更精准地还原细胞发育轨迹。通过对造血作用等过程的模拟，研究证明 scDiffEq 在细胞命运预测、时间点插值以及虚拟基因扰动分析方面均优于现有技术。此外，该工具展现出卓越的计算可扩展性，可处理超过百万级数量的细胞数据，并能广泛应用于从单时间点快照到多时间点谱系追踪的多种实验场景。这项技术为探索基因调控逻辑提供了一个高分辨率的动态视角，有助于深入理解发育生物学与疾病机制。References: Vinyard, M.E., Rasmussen, A.W., Li, R. et al. Learning cell dynamics with neural differential equations. Nat Mach Intell 7, 1969–1984 (2025). doi.org前往小宇宙评论区与主播互动
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392-scDiffEq：利用神经随机微分方程学习细胞动力学
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