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Explore computational approaches to DNA methylation analysis in this conference talk that examines how epigenetic signals can be leveraged for medical applications. Learn about two complementary research directions: utilizing methylation risk scores for disease prediction and imputing missing phenotypes from electronic health records, and employing methylation data for association analysis where tissue heterogeneity often obscures signals of interest. Discover how dimensionality reduction and deconvolution techniques enable the identification of cell-type-specific disease signals from bulk methylation measurements. Examine the intersection of prediction, interpretability, and heterogeneous biological data while understanding how DNA methylation reflects both genetic and environmental influences. Gain insights into open computational questions in this rapidly evolving field that sits at the crossroads of computational biology, machine learning, and precision medicine.
