网络多层集成工具箱 (MINT)
Saman Sarraf1, Bárbara Avelar-Pereira2,3, S M Hadi Hosseini2
1Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, USA. ssarraf@stanford.edu.
Communications biology
|June 7, 2025
概括
本研究介绍了MINT,这是一个用于整合多式模式阿尔茨海默病 (AD) 数据的Python工具箱. MINT有效地识别出不同的患者群体,并使用神经成像和液体生物标志物预测疾病进展.
科学领域:
- 计算生物学是一种计算生物学.
- 神经科学是一个神经科学.
- 生物信息学是一种生物信息学.
背景情况:
- 阿尔茨海默病 (AD) 诊断依赖于各种数据类型,包括神经成像,液体生物标志物和遗传学.
- 整合多式联运数据对于理解AD复杂性和识别有风险的个体至关重要.
- 现有的分析工具可能无法完全捕捉这些数据模式中的和这些数据模式之间的复杂关系.
研究的目的:
- 引入多层网络集成工具箱 (MINT),这是一个Python包,旨在实现多模式数据集成和社区检测.
- 评估MINT在改善阿尔茨海默病预测和通过建模 intra-和 inter-modality 关联来识别临床前病例方面的能力.
- 证明MINT在发现异质和多因素疾病中的复杂关系方面的实用性.
主要方法:
- 开发MINT,一个包含数据标准化,相似性网络融合和通用卢瓦恩集群的Python包.
- 将MINT应用于两个阿尔茨海默病 (AD) 队列 (n=206和n=143),使用结构MRI,PET,CSF,认知和遗传数据.
- 优化模式选择和交叉验证,以确定最佳的数据组合用于分析.
主要成果:
- MINT认为PET和CSF是阿尔茨海默病 (AD) 分析中最有信息的方法.
- 检测到两个不同的群体:一个AD主导和一个认知正常主导 (CN).
- 在对CN和AD组进行分类时,获得了高灵敏度 (84.38%) 和高特异性 (92.65%). 在AD占主导地位的社区中,AD病理标志物显著增加,认知能力较差. 值得注意的是,AD主导群体内的认知正常个体与CN主导群体中的同行相比,表现出高粉样蛋白和病理.
结论:
- 在阿尔茨海默氏症 (AD) 等复杂疾病中,MINT 是整合多式联络数据的强大工具.
- 该工具箱可以识别生物学相关的子组,并预测疾病的进展.
- MINT促进了跨异质数据的复杂关系的发现,有助于理解多因素障碍.
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