Penalized FCI for Causal Structure Learning in a Sparse DAG for Biomarker Discovery in Parkinson's Disease
用于帕金森病生物标志物发现的稀疏因果结构学习：惩罚化 FCIAI 解读 ↓
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用于帕金森病生物标志物发现的稀疏因果结构学习：惩罚化 FCI
- 研究问题
- 在高维血浆与脑脊液标志物中，如何学习与帕金森病相关的稀疏因果结构，并区分病理过程的标志与疾病进展的后果？
- 方法与思路
- 作者提出惩罚化快速因果推断算法 PFCI，将稀疏性约束纳入因果结构学习，并应用于包含大量生物标志物的 PPMI 数据。
- 材料陈述的贡献
- 摘要提出面向高维稀疏生物数据的因果发现方法，旨在寻找与帕金森病病理有因果联系的标志物；未报告具体发现或性能结果。
- 阅读关注点
- 阅读全文时应核对惩罚项、条件独立检验、潜在混杂处理及计算复杂度，并检查所学图结构如何支持标志物的因果解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。摘要未说明因果发现的具体假设、验证结果或临床效用，不能据此确认标志物有效。
原文摘要
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.