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通过交叉模式的变压器重建来实现未配对的多组组的可解释集成,用于阿尔茨海默氏症的诊断
Kai Liao1,2,3,4, Danfeng Du5, Jiawei Li6
1The Central Laboratory of Birth Defects Prevention and Control, The Affiliated Women and Children's Hospital of Ningbo University, Ningbo, China.
PLoS computational biology
|March 12, 2026
概括
AE-Trans集成了未配对的转录和表观遗传数据用于阿尔茨海默病 (AD) 检测. 这种可解释的框架显示了早期诊断和AD生物标志物发现的高准确性和概括性.
科学领域:
- 神经科学是一个神经科学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 阿尔茨海默病 (AD) 是一种进展性神经退行性疾病,诊断工具有限.
- 整合未配对的转录和表观遗传数据用于AD研究是具有挑战性的,因为数据异质性和小样本大小.
研究的目的:
- 开发一个可解释的框架,AE-Trans,用于整合未配对RNA和DNA甲基化数据用于阿尔茨海默病研究.
- 通过多omics数据集成,增强早期检测,生物标志物发现和阿尔茨海默病风险预测.
主要方法:
- AE-Trans采用双通道变压器框架,具有交叉模式重建和多头注意力,以对齐RNA和DNA甲基化数据.
- 该模型在前额叶皮和皮数据集上得到验证,包括外部未配对和现实世界配对的多omics数据集.
- 可解释性是通过反事实集成梯度来确定关键生物特征来实现的.
主要成果:
- 在前额叶皮层数据集上,AE-Trans实现了高精度 (0.9736) 和AUC (0.9910),并对皮层队列表现出强大的概括性 (精度0.7389,AUC 0.8432).
- 对外部未配对 (精度0.87,AUC0.94) 和配对 (精度0.88,AUC0.93) 数据集的验证证实了该模型的稳定性.
- 确定了与免疫调节,荷尔蒙信号和神经元代谢相关的关键特征,通过途径丰富和后勤回归 (AUC 0.9749) 进行验证.
- 在独立的RNA数据集和根据预后分层的患者上,AE-Trans改善了分类 (AUC 0.92,0.89).
结论:
- AE-Trans是一个强大且易于解释的工具,用于在阿尔茨海默氏症研究中整合多种OMIC.
- 该框架支持早期诊断,生物标志物发现和对阿尔茨海默病的个性化风险预测.
- AE-Trans有效地整合了未配对的转录组和表观遗传数据,克服了多组分析中的常见挑战.
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