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解读细胞命运的层次调节网络通过在单细胞多组数据上通过表观遗传学告知异质图形变压器解读单细胞多组数据
Yuhong Huang1, Chao Liu1, Zhiling Yang2
1Department of Oral Pathology, School of Stomatology, Dalian Medical University, No. 9, Western Section of Lushun South Road, Dalian, Liaoning 116044, China.
Briefings in bioinformatics
|February 5, 2026
概括
我们开发了SMOGT,这是一种新的图形学习方法,用于绘制控制细胞命运的等级调节网络. SMOGT准确地预测了基因调节和染色质相互作用,揭示了发育和疾病的关键驱动因素.
科学领域:
- * 计算生物学 * 计算生物学
- * 系统生物学 系统生物学
- * 基因组学 是一个学科.
背景情况:
- * 细胞命运决定由复杂的层次调节网络 (HRNets) 控制,其中包括转录因子 (TF) 和 cis调节元素 (CREs).
- * 现有的单细胞多组学方法难以捕捉这些调节网络的层次和因果性质.
- *了解HRNets对于破译细胞命运动态在发育和疾病中至关重要.
研究的目的:
- * 开发一种新的计算方法,即SMOGT (单细胞多态图形变压器),用于破译层次监管网络 (HRNets).
- *使用单细胞多基因数据准确地建模TF,CRE和基因 (TG) 之间的因果关系.
- * 预测细胞命运轨迹,并确定驱动细胞转变的调节机制.
主要方法:
- * 实现了SMOGT,一种使用异质图形变压器 (HGT) 的图形表示学习方法.
- * 结构化的信息沿着一个层次化的元路径 (TF-TF → TF-CRE → CRE-CRE → CRE-TG) 流动,以嵌入表观遗传机制.
- *采用半监督策略,并根据ChIP-seq和HiC-seq数据进行验证.
主要成果:
- *与现有方法相比,SMOGT在预测转录调节 (TF-CRE) 和染色质构成 (CRE-CRE) 中的准确性显著更高.
- *多层随机步行 (MRWR) 模块确定了驱动器调节器及其目标基因.
- * 生物流网模块成功预测了在in silico扰动后的细胞命运转移.
- * 应用于造血干细胞分化,黑色素瘤EMT和急性髓性白血病 (AML),SMOGT阐明了因果级联,确定了治疗窗口,并揭示了预后CREs.
结论:
- * SMOGT准确地建模了基因调节网络中的等级因果关系,为剖析细胞命运动态提供了强大的工具.
- *该方法成功预测了调节机制,并确定了各种生物过程和疾病中的关键参与者.
- * SMOGT提供了一种强大的方法来理解和预测正常发育和病理条件下的细胞命运过渡.
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