Feng Yixue, Liu Kefei, Kim Mansu, Long Qi, Yao Xiaohui, Shen Li
School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, USA.
Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
Proc IEEE Int Symp Bioinformatics Bioeng. 2020 Oct;2020:308-314. doi: 10.1109/bibe50027.2020.00057. Epub 2020 Dec 16.
We present an effective deep multiview learning framework to identify population structure using multimodal imaging data. Our approach is based on canonical correlation analysis (CCA). We propose to use deep generalized CCA (DGCCA) to learn a shared latent representation of non-linearly mapped and maximally correlated components from multiple imaging modalities with reduced dimensionality. In our empirical study, this representation is shown to effectively capture more variance in original data than conventional generalized CCA (GCCA) which applies only linear transformation to the multi-view data. Furthermore, subsequent cluster analysis on the new feature set learned from DGCCA is able to identify a promising population structure in an Alzheimer's disease (AD) cohort. Genetic association analyses of the clustering results demonstrate that the shared representation learned from DGCCA yields a population structure with a stronger genetic basis than several competing feature learning methods.
我们提出了一种有效的深度多视图学习框架，用于使用多模态成像数据识别群体结构。我们的方法基于典型相关分析（CCA）。我们建议使用深度广义CCA（DGCCA）从多个成像模态中学习非线性映射且最大相关组件的共享潜在表示，并降低维度。在我们的实证研究中，与仅对多视图数据应用线性变换的传统广义CCA（GCCA）相比，这种表示能够有效地捕获原始数据中更多的方差。此外，对从DGCCA学习到的新特征集进行后续聚类分析，能够在阿尔茨海默病（AD）队列中识别出有前景的群体结构。聚类结果的遗传关联分析表明，从DGCCA学习到的共享表示产生的群体结构比几种竞争的特征学习方法具有更强的遗传基础。
