@article{6f21d67031ba4d76afe0463f155c455f,
title = "High-dimensional integrative copula discriminant analysis for multiomics data",
abstract = "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.",
keywords = "Gaussian copula, data mining, discriminant analysis, integrative analysis, machine learning",
author = "Yong He and Hao Chen and Hao Sun and Jiadong Ji and Yufeng Shi and Xinsheng Zhang and Lei Liu",
note = "Funding Information: Research reported in this publication was supported by the National Key R&D Program of China (Grant No. 2018YFA0703900), National Science Foundation of China (11801316, 81803336, 11871309, 11971116), Natural Science Foundation of Shandong Province (ZR2019QA002, ZR2018BH033) and National Statistical Scientific Research Project (2018LY63). Research reported in this publication was also supported by NIH UL1 TR002345 and R21 AG063370. The content is solely the responsibility of the authors and does not necessarily represent the official view of the NIH. The results published here are in whole or in part based on data obtained from the AMP‐AD Knowledge Portal ( https://adknowledgeportal.synapse.org/ ). Study data were provided by the Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago. The authors thank the patients and their families for their selfless donation to further understanding Alzheimer's disease. The funding body played no role in the design, writing, or decision to publish this manuscript. The authors would like to thank the Editor, the Associate Editor, and the anonymous reviewer for their constructive comments that led to a major improvement of this article. Funding Information: information Foundation for the National Institutes of Health, UL1 TR002345; National Key R&D Program of China, 2018YFA0703900; National Natural Science Foundation of China, 11801316; 11871309; 11971116; 81803336; Natural Science Foundation of Shandong Province, ZR2018BH033; ZR2019QA002Research reported in this publication was supported by the National Key R&D Program of China (Grant No. 2018YFA0703900), National Science Foundation of China (11801316, 81803336, 11871309, 11971116), Natural Science Foundation of Shandong Province (ZR2019QA002, ZR2018BH033) and National Statistical Scientific Research Project (2018LY63). Research reported in this publication was also supported by NIH UL1 TR002345 and R21 AG063370. The content is solely the responsibility of the authors and does not necessarily represent the official view of the NIH. The results published here are in whole or in part based on data obtained from the AMP-AD Knowledge Portal (https://adknowledgeportal.synapse.org/). Study data were provided by the Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago. The authors thank the patients and their families for their selfless donation to further understanding Alzheimer's disease. The funding body played no role in the design, writing, or decision to publish this manuscript. The authors would like to thank the Editor, the Associate Editor, and the anonymous reviewer for their constructive comments that led to a major improvement of this article. Funding Information: Foundation for the National Institutes of Health, UL1 TR002345; National Key R&D Program of China, 2018YFA0703900; National Natural Science Foundation of China, 11801316; 11871309; 11971116; 81803336; Natural Science Foundation of Shandong Province, ZR2018BH033; ZR2019QA002 Funding information Publisher Copyright: {\textcopyright} 2020 John Wiley & Sons, Ltd.",
year = "2020",
month = dec,
day = "30",
doi = "10.1002/sim.8758",
language = "English",
volume = "39",
pages = "4869--4884",
journal = "Statistics in Medicine",
issn = "0277-6715",
number = "30",
}