MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations

Hunter J. Meltona Department of Statistics, Florida State University, Tallahassee, FL, USAView further author information

Zichen Zhangb Department of Biostatistics, The University of Texas MD Anderson Cancer CenterView further author information

Hong-Wen Dengc Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, USAView further author information

Lang Wud Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USACorrespondence[email protected]
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Chong Wub Department of Biostatistics, The University of Texas MD Anderson Cancer Center;e Institute for Data Science in Oncology, The UT MD Anderson Cancer Center, Houston, TX, USACorrespondence[email protected]

https://orcid.org/0000-0002-8400-1785 View further author information

MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations

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MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations

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