基于肠道菌群的机器学习模型，提升帕金森病早期诊断准确率
BDPM: A Machine Learning-Based Feature Extractor for Parkinson's Disease Classification via Gut Microbiota Analysis
- 融合随机森林与递归特征消除，结合生态学知识筛选关键菌群
- 在39名患者与健康对照中验证，有效识别差异菌群特征
- 适合神经退行性疾病研究者及精准医疗领域应用
帕金森病仍是一种高误诊率的神经退行性疾病，主要依赖临床评分量表。近年研究显示肠道菌群与帕金森病存在强关联，提示其可能作为潜在生物标志物。尽管基于肠道菌群的深度学习模型在早期预测中展现潜力，但多数方法仅使用单一分类器，常忽略菌株间关联或时间动态变化。因此，亟需更稳健的微生物组数据特征提取方法。本研究提出BDPM（基于机器学习的帕金森病分类特征提取器）。首先，收集39名帕金森病患者及其健康配偶的肠道菌群谱，识别差异丰度类群。其次，开发名为RFRE（随机森林结合递归特征消除）的创新特征选择框架，融入生态学知识以增强生物学可解释性。最后，设计混合分类模型，捕捉微生物组数据中的时空模式。
原文摘要 · Abstract (English)
Background: Parkinson's disease remains a major neurodegenerative disorder with high misdiagnosis rates, primarily due to reliance on clinical rating scales. Recent studies have demonstrated a strong association between gut microbiota and Parkinson's disease, suggesting that microbial composition may serve as a promising biomarker. Although deep learning models based ongut microbiota show potential for early prediction, most approaches rely on single classifiers and often overlook inter-strain correlations or temporal dynamics. Therefore, there is an urgent need for more robust feature extraction methods tailored to microbiome data. Methods: We proposed BDPM (A Machine Learning-Based Feature Extractor for Parkinson's Disease Classification via Gut Microbiota Analysis). First, we collected gut microbiota profiles from 39 Parkinson's patients and their healthy spouses to identify differentially abundant taxa. Second, we developed an innovative feature selection framework named RFRE (Random Forest combined with Recursive Feature Elimination), integrating ecological knowledge to enhance biological interpretability. Finally, we designed a hybrid classification model to capture temporal and spatial patterns in microbiome data.
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