A SuperLearner-based pipeline for the development of DNA methylation-derived predictors of phenotypic traits
We introduce a novel method for the development of DNAm-based predictors that combines the improved reliability conferred by training on principal components with advanced ensemble-based machine learning. Coupling SuperLearner with PCA in the predictor development process may be especially relevant for studies with longitudinal designs utilizing multiple array types, as well as for the development of predictors of more complex phenotypic traits.
Authors
- Lars van der Laan (ORCID: https://orcid.org/0000-0001-8220-6210)
- Nina Holland (ORCID: https://orcid.org/0000-0003-3284-8156)
- Dennis Khodasevich (ORCID: https://orcid.org/0000-0003-1412-8251)
- Andrés Cárdenas (ORCID: https://orcid.org/0000-0003-2284-3298)
Institutions
- University of Washington (US)
- Center for Environmental Health (US)
- University of California, Berkeley (US)
- Stanford University (US)
Publication Details
- Journal
- PLoS Computational Biology
- Published
- 2025-02-06
- DOI
- https://doi.org/10.1371/journal.pcbi.1012768
- Citations
- 1
- Primary Topic
- Epigenetics and DNA Methylation
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- U.S. Environmental Protection Agency
- National Institutes of Health
- National Institute on Aging
- National Institute of Environmental Health Sciences
- National Institute on Minority Health and Health Disparities