Multiomics or integrative omics data have been increasingly common in biomedical studies, holding a promise in better understanding human health and disease. In this article, we propose an integrative copula discrimination analysis classifier in the context of two‐class classification, which relaxes the common Gaussian assumption and gains power by borrowing information from multiple omics data types in discriminant analysis. Numerical studies are conducted to assess the finite sample performance of the new classifier. We apply our model to the Religious Orders Study and Memory and Aging Project (ROSMAP) Study, integrating gene expression and DNA methylation data for better prediction.

中文翻译：

---

多组学数据的高维综合copula判别分析

多组学或综合组学数据在生物医学研究中越来越普遍，有望更好地了解人类健康和疾病。在本文中，我们在二分类的背景下提出了一个综合的 copula 判别分析分类器，它放宽了常见的高斯假设，并通过在判别分析中从多个组学数据类型中借用信息来获得力量。进行数值研究以评估新分类器的有限样本性能。我们将我们的模型应用于宗教秩序研究和记忆与衰老项目 (ROSMAP) 研究，整合基因表达和 DNA 甲基化数据以进行更好的预测。