Zhuang Junli, Tian Jinping, Xiong Xiaoxing, Li Taihan, Chen Zhengwei, Chen Rong, Chen Jun, Li Xiang
Department of Vascular Surgery, Renmin Hospital of Wuhan University, Wuhan, China.
Faculty of Medicine, Jianghan University, Wuhan, China.
Front Aging Neurosci. 2023 Mar 2;15:1052783. doi: 10.3389/fnagi.2023.1052783. eCollection 2023.
Alzheimer's disease (AD) is a severe neurodegenerative disease for which there is currently no effective treatment. Mild cognitive impairment (MCI) is an early disease that may progress to AD. The effective diagnosis of AD and MCI in the early stage has important clinical significance.
To this end, this paper proposed a hypergraph-based netNMF (HG-netNMF) algorithm for integrating structural magnetic resonance imaging (sMRI) of AD and MCI with corresponding gene expression profiles.
Hypergraph regularization assumes that regions of interest (ROIs) and genes were located on a non-linear low-dimensional manifold and can capture the inherent prevalence of two modalities of data and mined high-order correlation features of the two data. Further, this paper used the HG-netNMF algorithm to construct a brain structure connection network and a protein interaction network (PPI) with potential role relationships, mine the risk (ROI) and key genes of both, and conduct a series of bioinformatics analyses.
Finally, this paper used the risk ROI and key genes of the AD and MCI groups to construct diagnostic models. The AUC of the AD group and MCI group were 0.8 and 0.797, respectively.
阿尔茨海默病（AD）是一种严重的神经退行性疾病，目前尚无有效治疗方法。轻度认知障碍（MCI）是一种可能进展为AD的早期疾病。早期有效诊断AD和MCI具有重要的临床意义。
为此，本文提出了一种基于超图的netNMF（HG-netNMF）算法，用于将AD和MCI的结构磁共振成像（sMRI）与相应的基因表达谱进行整合。
超图正则化假设感兴趣区域（ROI）和基因位于非线性低维流形上，能够捕捉两种数据模态的内在普遍性，并挖掘两种数据的高阶相关特征。此外，本文使用HG-netNMF算法构建具有潜在作用关系的脑结构连接网络和蛋白质相互作用网络（PPI），挖掘两者的风险（ROI）和关键基因，并进行一系列生物信息学分析。
最后，本文使用AD组和MCI组的风险ROI和关键基因构建诊断模型。AD组和MCI组的AUC分别为0.8和0.797。
