融合图注意力与变分自编码的可解释框架，提升小样本下阿尔茨海默病生物标志物发现能力。
An Interpretable Ensemble Framework for Multi-Omics Dementia Biomarker Discovery Under HDLSS Conditions
- 组合GAT、MOVE、弹性网与FDR，构建多组学数据集成分析框架。
- 在模拟数据和ADNI数据上均实现更高预测精度与特征选择准确率。
- 生成可解释的基因网络图，揭示痴呆潜在分子机制，适合神经疾病研究者。
神经退行性疾病中的生物标志物发现需要在低样本条件下处理高维度多组学数据，且具备鲁棒性与可解释性。本文提出一种新型集成框架，融合图注意力网络（GAT）、多组学变分自编码器（MOVE）、弹性网稀疏回归及Storey假阳性发现率（FDR）方法。该框架在模拟多组学数据与阿尔茨海默病影像遗传学计划（ADNI）数据集上，与当前先进方法（DIABLO、MOCAT、AMOGEL、MOMLIN）进行对比评估。结果表明，本方法在预测准确性、特征选择精度及生物学相关性方面均表现更优。基于两个数据集推导出的生物标志基因图谱被可视化并解读，为痴呆的潜在分子机制提供了新见解。
原文摘要 · Abstract (English)
Biomarker discovery in neurodegenerative diseases requires robust, interpretable frameworks capable of integrating high-dimensional multi-omics data under low-sample conditions. We propose a novel ensemble approach combining Graph Attention Networks (GAT), MultiOmics Variational AutoEncoder (MOVE), Elastic-net sparse regression, and Storey's False Discovery Rate (FDR). This framework is benchmarked against state-of-the-art methods including DIABLO, MOCAT, AMOGEL, and MOMLIN. We evaluate performance using both simulated multi-omics data and the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our method demonstrates superior predictive accuracy, feature selection precision, and biological relevance. Biomarker gene maps derived from both datasets are visualized and interpreted, offering insights into latent molecular mechanisms underlying dementia.
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