Abstract
Epigenetic modifications link the environment to gene expression and play a crucial role in tumour development. DNA methylation, in particular, is gaining attention in cancer research, including cervical cancer, the focus of this study. Public repositories such as The Cancer Genome Atlas (TCGA) provide extensive genetic profiles but comparatively limited epigenetic data. We propose a new method, called multivariate classified mixed model prediction (mvCMMP), a multivariate nested-error regression framework for predicting DNA methylation from genetic data in cervical cancer. mvCMMP exploits dependencies among outcomes and class-specific random effects associated with new observations. We show that mvCMMP improves prediction accuracy over competing methods, highlighting the benefits of borrowing strength across methylation markers and shared random effects.
| Original language | English (US) |
|---|---|
| Journal | Canadian Journal of Statistics |
| DOIs | |
| State | Accepted/In press - 2025 |
Bibliographical note
Publisher Copyright:© 2025 The Author(s). The Canadian Journal of Statistics|La revue canadienne de statistique published by Wiley Periodicals LLC on behalf of Statistical Society of Canada | Société statistique du Canada.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- DNA methylation
- mixed model prediction
- multivariate outcome
- unbalanced sample
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