用多组学数据构建慢性疲劳综合征诊断模型，揭示病征与微生物免疫代谢的关联。
Advancing Digital Precision Medicine for Chronic Fatigue Syndrome through Longitudinal Large-Scale Multi-Modal Biological Omics Modeling with Machine Learning and Artificial Intelligence
- 基于纵向多组学数据，用深度学习建模症状与生物标志物关系。
- 实现疾病分类的最先进精度，并重构出关键症状特征。
- 首次绘制健康与疾病状态下的多组学互作图谱，适合精准医疗研究者。
我们研究了慢性病（如慢性疲劳综合征ME/CFS和长期新冠）具有高度异质性、多因素病因及进展复杂的问题，难以诊断与治疗。为此，我们开发了BioMapAI，一个可解释的深度学习框架，利用迄今最丰富的纵向多组学数据集进行建模，涵盖肠道菌群宏基因组、血浆代谢组、免疫谱、血液检测指标及临床症状。通过将多组学数据与症状矩阵关联，BioMapAI识别出疾病特异性和症状特异性生物标志物，重构了症状表现，并实现了最先进的疾病分类精度。我们还首次构建了健康与疾病状态下多组学之间的连接图谱，揭示了从健康到ME/CFS过程中微生物-免疫-代谢串扰的变化机制。
原文摘要 · Abstract (English)
We studied a generalized question: chronic diseases like ME/CFS and long COVID exhibit high heterogeneity with multifactorial etiology and progression, complicating diagnosis and treatment. To address this, we developed BioMapAI, an explainable Deep Learning framework using the richest longitudinal multi-omics dataset for ME/CFS to date. This dataset includes gut metagenomics, plasma metabolome, immune profiling, blood labs, and clinical symptoms. By connecting multi-omics to a symptom matrix, BioMapAI identified both disease- and symptom-specific biomarkers, reconstructed symptoms, and achieved state-of-the-art precision in disease classification. We also created the first connectivity map of these omics in both healthy and disease states and revealed how microbiome-immune-metabolome crosstalk shifted from healthy to ME/CFS.
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