Jing‐Yu Fan

The paper expands the DNA motif lexicon of transcriptional regulatory code by identifying composite elements and using a deep learning model to predict their activity and impact of variants.

The paper identifies transcription factor networks driving fibrotic lung disease using multiomic single-cell data from SSc-ILD and control lungs.

MetaTiME identifies data-driven meta-components in tumor immune microenvironment scRNA-seq data to annotate cell states and reveal transcriptional regulators.

The paper identifies two classes of transcription factors based on their regulatory range and shows that long-range TFs' activity depends on chromatin state.

Lisa predicts transcriptional regulators of gene sets by integrating chromatin accessibility and ChIP-seq data to identify relevant regulators.