用图模型发现阿尔茨海默病血液生物标志物新组合，提升诊断与药物靶点发现效率。
Graph-Based Biomarker Discovery and Interpretation for Alzheimer's Disease
- 构建生物标志物图网络，联合优化诊断准确率与标志物发现。
- 在公开数据中识别出3个差异交互的新型标志物子网络。
- 适合临床研究者和药物研发人员用于挖掘潜在治疗靶点。
阿尔茨海默病（AD）的早期诊断与治疗靶点发现对有效管理至关重要。当前基于影像学的诊断方法成本高昂、可及性差，难以用于大规模筛查。近年来，血液检测在AD诊断中展现出潜力，并可能揭示可用于药物干预的生物标志物，具有更低成本、更高可及性与微创优势。然而，由于各类生物标志物之间存在重要关联，标志物发现过程仍具挑战。本文提出BRAIN（Biomarker Representation, Analysis, and Interpretation Network），一种新的机器学习框架，通过全貌化的图结构表示生物标志物，揭示其相互依赖关系，并解释为何不同模型会识别出不同的判别性标志物。我们将BRAIN应用于公开的血液生物标志物数据集，发现三个新型生物标志物子网络，其交互模式在对照组与AD组间存在显著差异，为AD药物发现与标志物分析提供了新范式。
原文摘要 · Abstract (English)
Early diagnosis and discovery of therapeutic drug targets are crucial objectives for effective management of Alzheimer's Disease (AD). Current approaches for AD diagnosis and treatment planning are based on radiological imaging and largely inaccessible for population-level screening due to prohibitive costs and limited availability. Recently, blood tests have shown promise in diagnosing AD and highlighting possible biomarkers that can be used as drug targets for AD management. Blood tests are significantly more accessible to disadvantaged populations, cost-effective, and minimally invasive. However, biomarker discovery in the context of AD diagnosis is complex as there exist important associations between various biomarkers. Here, we introduce BRAIN (Biomarker Representation, Analysis, and Interpretation Network), a novel machine learning (ML) framework to jointly optimize diagnostic accuracy and biomarker discovery processes to identify all relevant biomarkers that contribute to AD diagnosis. Using a holistic graph-based representation for biomarkers, we highlight their interdependencies and explain why different ML models identify different discriminative biomarkers. We apply BRAIN to a publicly available blood biomarker dataset, revealing three novel biomarker subnetworks whose interactions vary between the control and AD groups, offering a new paradigm for drug discovery and biomarker analysis for AD.
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