High-dimensional structured data are emerging and accumulating in biomedical research fields. Examples include epigenomics and neuroimaging studies. In these studies, it is often required to extract biologically meaningful patterns and identify relevant biological features from highdimensional structured data. Motivated by this problem, we propose a new statistical learning method named Principal Wave Analysis (PWA). The practical merits of PWA are shown through simulation studies incorporating diverse types of signal patterns as well as its applications to epigenomic and neuroimaging data.

中文翻译：

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应用于表观基因组学和神经影像学研究的高维结构化数据的主波分析

高维结构化数据在生物医学研究领域不断涌现和积累。例子包括表观基因组学和神经影像学研究。在这些研究中，通常需要从高维结构化数据中提取具有生物学意义的模式并识别相关的生物学特征。受这个问题的启发，我们提出了一种新的统计学习方法，称为主波分析（PWA）。PWA 的实际优点通过包含不同类型信号模式的模拟研究及其在表观基因组和神经成像数据中的应用来展示。