这篇文章介绍了一款名为 TrimNN 的深度学习框架，它旨在通过识别 细胞群落 (CC) 基序 来分析复杂组织中的细胞空间组织。TrimNN 采用 自下而上 的方法，利用 三角剖分 来识别不同大小的保守空间细胞组织模式。与传统的聚类方法不同，TrimNN 能够提供 可解释的拓扑信息，揭示细胞类型模式与疾病表型之间的关键关联。研究人员利用 TrimNN 在结直肠癌和阿尔茨海默病等多种空间组学案例中，成功识别了与疾病进展和患者预后相关的细胞基序，证明了其在理解疾病机制和发现潜在生物标志物方面的 强大能力。References: Yu Y, Wang S, Li J, et al. TrimNN: Characterizing cellular community motifs for studying multicellular topological organization in complex tissues[J]. Research Square, 2025: rs. 3. rs-5584635.
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