Chengyu Liu

The paper investigates the transcriptional effects of rs11607019 on gene expression in 293 cells, finding it upregulates several genes and downregulates APOBEC3G.

The paper predicts somatic mutation rates in 13 cancers using DNA motifs and identifies impactful motifs and cancer-specific regions for classification.

This study shows that MLL3/MLL4 methyltransferase activities are essential for early embryonic development and stem cell differentiation, but their role is lineage-selective and not dependent on enhancer activation.

The paper develops a framework to derive linear formulas linking DNA motifs to gene expression using an interpretable neural network model, identifying 300 motifs with regulatory roles across 154 cell types.

The paper identifies DNA motifs associated with methylation and unmethylation and shows their functional relevance in DNA methylation regulation and cancer.

This study shows that H3.3K4M destabilizes MLL3/4 proteins, preventing enhancer activation and impairing adipose tissue development.

The paper introduces contextual regression, a nonlinear model that combines interpretability and accuracy, demonstrating its effectiveness on simulated data and open chromatin prediction.