The effective and accurate diagnosis of Alzheimer's disease (AD), especially in the early stage (i.e., mild cognitive impairment (MCI)) remains a big challenge in AD research. So far, multiple biomarkers have been associated with AD diagnosis and progression. However, most of the existing research only utilized single modality data for diagnostic biomarker identification, which did not take the advantages of multi-modal data that provide comprehensive and complementary information at multiple levels into consideration. In this paper, we integrate multi-modal genomic data from postmortem AD brains (i.e., mRNA, miRNA and epigenomic data) and propose a hyper-graph based sparse canonical correlation analysis (HGSCCA) method to extract the most correlated multi-modal biomarkers associated with AD and MCI. Specifically, our model utilizes the sparse canonical correlation analysis framework (SCCA), which aims at finding the best linear projections for each input modality so that the strongest correlation within the selected features of multi-dimensional genomic data can be captured. In addition, with the consideration of high-order relationships among different subjects, we also introduce a hyper-graph-based regularization term that will lead to the selection of more discriminative biomarkers. To evaluate the effectiveness of the proposed method, we conduct the experiments on the well-known AD cohort study, The Religious Orders Study and Memory and Aging Project (ROSMAP) dataset, and the results show that our method can not only identify meaningful biomarkers for the diagnosis AD disease, but also achieve superior classification performance than the comparing methods.

中文翻译：

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基于超图的稀疏典型相关分析从多维基因组数据诊断阿尔茨海默病


阿尔茨海默病（AD）的有效和准确诊断，尤其是早期阶段（即轻度认知障碍（MCI））仍然是AD研究中的一大挑战。到目前为止，多种生物标志物已与 AD 诊断和进展相关。然而，现有研究大多仅利用单一模态数据进行诊断生物标志物识别，没有考虑到多模态数据在多个层面上提供全面且互补的信息的优势。在本文中，我们整合了死后 AD 大脑的多模态基因组数据（即 mRNA、miRNA 和表观基因组数据），并提出了一种基于超图的稀疏典型相关分析 (HGSCCA) 方法，以提取相关性最高的多模态生物标志物与 AD 和 MCI。具体来说，我们的模型利用稀疏典型相关分析框架（SCCA），旨在为每种输入模态找到最佳线性投影，以便捕获多维基因组数据的选定特征内最强的相关性。此外，考虑到不同主题之间的高阶关系，我们还引入了基于超图的正则化项，这将导致选择更具辨别力的生物标志物。为了评估所提出方法的有效性，我们在著名的 AD 队列研究、The Religious Orders Study 和 Memory and Aging Project (ROSMAP) 数据集上进行了实验，结果表明我们的方法不仅可以识别有意义的生物标志物诊断 AD 疾病，而且还取得了比比较方法更优越的分类性能。