Associating brain imaging phenotypes and genetic risk factors via a hypergraph based netNMF method - Zhuang, Junli; Tian, Jinping; Xiong, Xiaoxing; Li, Taihan; Chen, Zhengwei; Chen, Rong; Chen, Jun; Li, Xiang (Frontiers in Aging Neuroscience, 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 proposes 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 are located on a non-linear low-dimensional manifold and can capture the inherent prevalence of two modalities of data and mine high-order correlation features of the two data. Further, this paper uses 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 uses 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.