发表机构
University of California, Los Angeles; Institute for Quantitative and Computational Biosciences (QCBio), University of California, Los Angeles; Molecular Biology Institute, University of California, Los Angeles; West African Centre for Cell Biology of Infectious Pathogens (WACCBIP), Department of Biochemistry, Cell and Molecular Biology, College of Basic and Applied Sciences, University of Ghana（加州大学洛杉矶分校; 加州大学洛杉矶分校定量与计算生物科学研究所; 加州大学洛杉矶分校分子生物学研究所; 加纳大学基础与应用科学学院生物化学、细胞与分子生物学系西非传染病细胞生物学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该协议提出利用网络传播整合多组学数据，通过信号扩散和聚类富集，识别跨分子层共享的收敛分子网络，揭示核心生物学机制。
AI 中文摘要
表型结果源于跨分子层的协调变化。尽管多组学数据捕获了互补的分子信号，但由于基因水平上的重叠有限，将它们整合为连贯的生物学机制仍然具有挑战性。网络传播将每个数据集中的显著基因命中映射到先验知识分子相互作用网络上，并将其信号扩散到邻近基因。对传播网络进行数学整合，随后进行聚类和通路富集分析，揭示了共享的分子网络，从而定位了跨正交分子层捕获的核心生物学。
英文摘要
Phenotypic outcomes arise from coordinated changes across molecular layers. Although multi-omics data capture complementary molecular signals, integrating them into coherent biological mechanisms remains challenging due to limited overlap at the gene level. Network propagation maps significant gene hits from each dataset onto a prior knowledge molecular interaction network and diffuses their signals to neighboring genes. Mathematical integration of propagated networks, followed by clustering and pathway enrichment, reveals shared molecular networks, pinpointing the core biology captured across orthogonal molecular layers.
Comments21 pages, 3 figures
