标题： 惩罚的FCI用于帕金森病生物标志物发现中的稀疏DAG因果结构学习 显示英文标题

标题： Penalized FCI for Causal Structure Learning in a Sparse DAG for Biomarker Discovery in Parkinson's Disease

摘要： 帕金森病（PD）是一种进行性神经退行性疾病，目前缺乏可靠的早期生物标志物用于诊断、预后和治疗监测。 虽然脑脊液（CSF）生物标志物，如α-突触核蛋白种子扩增检测（alphaSyn-SAA），具有诊断潜力，但由于侵入性和特异性不完全，其临床应用受到限制。 血浆生物标志物提供了一种微创的替代方法，但它们在PD中的机制作用仍不清楚。 一个主要挑战是区分血浆生物标志物是否因果地反映原发性神经退行性过程，还是疾病进展的下游结果。 为了解决这个问题，我们利用帕金森病进展标志物倡议（PPMI）项目9000，包含2,924个血浆和CSF生物标志物，系统地推断与疾病状态的因果关系。 然而，这些生物标志物及其相互关系中只有一小部分实际上与疾病相关。 现有的因果发现算法，如快速因果推断（FCI）及其变体，在稀疏性下面对生物标志物数据集的高维性存在困难，限制了其可扩展性。 我们提出惩罚型快速因果推断（PFCI），一种新的方法，通过引入稀疏性约束，以高效地推断大规模生物数据集中的因果结构。 通过将PFCI应用于PPMI数据，我们旨在识别与PD病理学有因果联系的生物标志物，从而实现早期诊断和患者分层。 我们的研究结果将促进基于生物标志物的临床试验，并有助于神经保护疗法的发展。 显示英文摘要

摘要： Parkinson's disease (PD) is a progressive neurodegenerative disorder that lacks reliable early-stage biomarkers for diagnosis, prognosis, and therapeutic monitoring. While cerebrospinal fluid (CSF) biomarkers, such as alpha-synuclein seed amplification assays (alphaSyn-SAA), offer diagnostic potential, their clinical utility is limited by invasiveness and incomplete specificity. Plasma biomarkers provide a minimally invasive alternative, but their mechanistic role in PD remains unclear. A major challenge is distinguishing whether plasma biomarkers causally reflect primary neurodegenerative processes or are downstream consequences of disease progression. To address this, we leverage the Parkinson's Progression Markers Initiative (PPMI) Project 9000, containing 2,924 plasma and CSF biomarkers, to systematically infer causal relationships with disease status. However, only a sparse subset of these biomarkers and their interconnections are actually relevant for the disease. Existing causal discovery algorithms, such as Fast Causal Inference (FCI) and its variants, struggle with the high dimensionality of biomarker datasets under sparsity, limiting their scalability. We propose Penalized Fast Causal Inference (PFCI), a novel approach that incorporates sparsity constraints to efficiently infer causal structures in large-scale biological datasets. By applying PFCI to PPMI data, we aim to identify biomarkers that are causally linked to PD pathology, enabling early diagnosis and patient stratification. Our findings will facilitate biomarker-driven clinical trials and contribute to the development of neuroprotective therapies.