Xiong Yiwei, Wang Jingtao, Shang Xiaoxiao, Chen Tingting, Fraser Douglas D, Fonseca Gregory J, Rousseau Simon, Ding Jun
Meakins-Christe Laboratories, Research Institute of McGill University Health Centre, 1001 Decarie Boulevard, Montreal, QC H4A 3J1, Canada.
Meakins-Christe Laboratories, Research Institute of McGill University Health Centre, 1001 Decarie Boulevard, Montreal, QC H4A 3J1, Canada; Department of Medicine, Division of Experimental Medicine, McGill University, 1001 Decarie Boulevard, Montreal, QC H4A 3J1, Canada.
Cell Rep Methods. 2025 Apr 21;5(4):101022. doi: 10.1016/j.crmeth.2025.101022. Epub 2025 Apr 10.
Understanding the interplay among clinical variables-such as demographics, symptoms, and laboratory results-and their relationships with disease outcomes is critical for advancing diagnostics and understanding mechanisms in complex diseases. Existing methods fail to capture indirect or directional relationships, while existing Bayesian network learning methods are computationally expensive and only infer general associations without focusing on disease outcomes. Here we introduce random walk- and genetic algorithm-based network inference (RAMEN), a method for Bayesian network inference that uses absorbing random walks to prioritize outcome-relevant variables and a genetic algorithm for efficient network refinement. Applied to COVID-19 (Biobanque québécoise de la COVID-19), intensive care unit (ICU) septicemia (MIMIC-III), and COPD (CanCOLD) datasets, RAMEN reconstructs networks linking clinical markers to disease outcomes, such as elevated lactate levels in ICU patients. RAMEN demonstrates advantages in computational efficiency and scalability compared to existing methods. By modeling outcome-specific relationships, RAMEN provides a robust tool for uncovering critical disease mechanisms, advancing diagnostics, and enabling personalized treatment strategies.
了解临床变量（如人口统计学、症状和实验室检查结果）之间的相互作用及其与疾病结局的关系，对于推进复杂疾病的诊断和理解其机制至关重要。现有方法无法捕捉间接或方向性的关系，而现有的贝叶斯网络学习方法计算成本高昂，且仅推断一般关联，而不关注疾病结局。在此，我们介绍基于随机游走和遗传算法的网络推理（RAMEN），这是一种贝叶斯网络推理方法，它使用吸收随机游走对与结局相关的变量进行优先级排序，并使用遗传算法进行有效的网络优化。应用于COVID-19（魁北克COVID-19生物样本库）、重症监护病房（ICU）败血症（MIMIC-III）和慢性阻塞性肺疾病（COPD）（加拿大慢性阻塞性肺疾病队列研究）数据集时，RAMEN重建了将临床标志物与疾病结局联系起来的网络，如ICU患者乳酸水平升高。与现有方法相比，RAMEN在计算效率和可扩展性方面显示出优势。通过对特定结局的关系进行建模，RAMEN为揭示关键疾病机制、推进诊断和制定个性化治疗策略提供了一个强大的工具。
