Bryan Lunt

The paper models sequence-to-expression relationships in ER-bound enhancers to identify functional variants and regulatory mechanisms in the estrogen receptor transcriptional program.

The paper uses sequence-to-expression modeling to predict the impact of SNPs in an enhancer and validates mechanistic models with experimental data.

This study shows that regularization improves the generalizability of sequence-to-expression models by reducing overfitting in cross-validation experiments.