Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. With the help of machine learning algorithms and mechanistic modeling approaches, such as agent-based modeling, an in-depth analysis of the simulation of cell dynamics is possible as well as of high-dimensional omics resources indicative of pathway signaling changes. Here, we give a background on advances in research on brain-immune system cross-talk in Alzheimer's disease and review recent machine learning and mechanistic modeling approaches which leverage modern omics technologies for blood-based immune system-related biomarker discovery.

翻译：阿尔茨海默病在全球人群中的患病率持续上升，然而目前基于推荐生物标志物的诊断方法仅能在专科医疗机构开展。受此限制，该疾病通常在晚期才被确诊，这与现有治疗方案仅对早期患者有效的现状形成矛盾。基于血液的生物标志物有望填补便捷、低成本早期诊断方法的空白。鉴于近期发现中枢神经系统免疫细胞与外周免疫系统存在交互作用，免疫相关的血液生物标志物可能成为极具前景的研究方向。借助机器学习算法与基于智能体建模等机制建模方法，既可对细胞动力学模拟进行深入分析，亦能解析反映通路信号变化的高维度组学数据。本文系统阐述了阿尔茨海默病脑-免疫系统交互研究进展，并综述了近年来利用现代组学技术探索血液免疫相关生物标志物的机器学习与机制建模方法。