AI 中文总结
研究利用因果发现方法，联合建模辐射扰动与基因表达，构建有向基因网络，捕捉重要调控关系，揭示结构化网络特征及通路组织，凸显其在理解细胞辐射反应机制等方面的潜力。
AI 中文摘要
下一代测序技术，如RNA测序，可提供全基因组范围的基因表达测量，有助于广泛探索疾病和治疗反应的生物标志物及机制。处理此类数据的生物信息学工具大多是单变量、线性的，且依赖预定义的通路知识注释，限制了其捕捉非线性和多变量基因相互作用的能力。本文探索因果发现方法在表征人类细胞中辐射剂量率转录反应的应用。通过联合建模辐射扰动和基因表达，我们构建了有向基因网络，捕捉到超越相关性的重要调控关系，且与基线方法相比，显著富集了已知辐射反应通路。我们发现推断出的因果图揭示了如高入度管家基因和高出度转录因子等结构化网络特征。进一步分析表明了应激反应通路和触发细胞死亡通路的层次组织。这项工作凸显了因果发现在医疗环境中的潜力，可用于理解反应机制、识别调控靶点以及改善对复杂基因组数据的解读。
英文摘要
Next-generation sequencing technologies, including RNA-sequencing, provide genome-wide measurements of gene expression and enable broad explorations of biomarkers and mechanisms underlying disease and treatment response. Bioinformatics tools for processing this data, such as differential expression analysis, are largely univariate, linear, and rely on predefined pathway knowledge annotations, which limits their ability to capture nonlinear and multivariate gene interactions. This paper explores the application of causal discovery to characterizing transcriptional responses to radiation as a function of dose rate in human cells. By jointly modeling radiation perturbations and gene expression, we learn directed gene networks that capture important regulatory relationships beyond correlation and exhibit significant enrichment of known radiation response pathways compared to baseline approaches. We find that inferred causal graphs reveal structured network features such as high in-degree housekeeping genes and high out-degree transcription factors. Further analysis suggests a hierarchical organization of stress response pathways and triggered cell death pathways. This work highlights the potential of causal discovery in healthcare settings with applications to understanding response mechanisms, identifying regulatory targets, and improving interpretation of complex genomic data.
CommentsMachine Learning for Healthcare Conference
Journal refProceedings of Machine Learning Research [VOLUME # 340] 2026
