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AR-CDT NET:用于基于肠道微生物群的疾病分类的深度可变形卷积网络

Jiaye Li^1,2,3, Zijian Sun^2,3,4, Shuo Chai^2,3

¹College of Information Engineering, Zhejiang University of Technology, Liuhe Road, Hangzhou, 310023, Zhejiang, China.

BMC bioinformatics

|December 26, 2025

概括

一个新的深度学习框架,AR-CDT Net,使用肠道微生物组数据准确地分类疾病. 它有效地识别特定疾病的微生物特征,改善复杂疾病的差异诊断.

科学领域:

微生物组研究的研究.
计算生物学是一种计算生物学.
疾病诊断疾病诊断

背景情况:

肠道微生物组失调与复杂疾病有关,但计算诊断具有挑战性.
现有的方法难以处理大型,不平衡的微生物群数据集和捕捉微生物相互作用.
使用微生物组数据进行准确的差异诊断仍然是一个重要的未满足需求.

研究的目的:

开发一种新的深度学习框架,AR-CDT Net,以从肠道微生物群数据中准确和稳健地分类宿主疾病状态.
解决当前计算方法在预测性能,稳定性和交互捕获方面的局限性.
通过解开疾病特异性微生物特征,使精确的差异诊断成为可能.

主要方法:

开发了AR-CDT Net,这是一个深度学习框架,集成多尺度可变形卷积 (MS-DConv) 和通道智能动态tanh (CD-Tanh).
在一个大队列 (>8000个样本,8个表型) 中评估队列内表现.
在独立队列上验证的交叉数据集概括,包括异构的T2D队列.

主要成果:

在队列内部分类任务中,AR-CDT Net的表现优于九个代表模型.
在T2D队列的跨数据集概括中获得了0.7921的显著AUC,表明可转移的生物信号.
通过使用缩小维度和SHAP解释,成功地从共享的异生物背景中解开疾病特异性致病特征.

结论:

AR-CDT Net提供了一个强大的,准确的深度学习方法,用于基于微生物组的疾病分类.
该框架在数据集中展示了有效的概括,捕获可转移的微生物信号.
AR-CDT Net提供可解释的见解,区分疾病特异性的微生物模式,以改善差异诊断.

关键词:

深度学习是一种深度学习.疾病的分类疾病的分类.我们的肠道微生物组.微生物的签名是微生物的签名.在SHAP分析中,我们分析了SHAP.

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