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- Title
Associating brain imaging phenotypes and genetic risk factors via a hypergraph based netNMF method.
- Authors
Junli Zhuang; Jinping Tian; Xiaoxing Xiong; Taihan Li; Zhengwei Chen; Rong Chen; Jun Chen; Xiang Li
- Abstract
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. Methods: To this end, this paper proposed a hypergraph-based netNMF (HGnetNMF) algorithm for integrating structural magnetic resonance imaging (sMRI) of AD and MCI with corresponding gene expression profiles. Results: 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. Conclusion: 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.
- Subjects
BRAIN; COGNITION disorders; X-linked genetic disorders; ALZHEIMER'S disease; GENE expression; PEARSON correlation (Statistics); LOGISTIC regression analysis; DATA analysis software; PHENOTYPES; DISEASE risk factors
- Publication
Frontiers in Aging Neuroscience, 2023, Vol 15, p1
- ISSN
1663-4365
- Publication type
Article
- DOI
10.3389/fnagi.2023.1052783