David Mauduit

The paper evaluates computational methods for predicting cell type-specific enhancers in the mouse cortex using in vivo validation data and finds that open chromatin and sequence models improve prediction accuracy.

The paper compares enhancer codes of cell types between mammalian neocortex and avian pallium using deep learning, finding higher divergence in excitatory neurons than other cell types.

This study uses deep learning to design synthetic enhancers that target specific cell types in fruit flies and humans, revealing the enhancer code as optimal TF activator motifs and absence of repressor motifs.

The paper maps enhancer gene regulatory networks (GRNs) in liver cell types and identifies TFs governing hepatocyte zonation states using multiomics and deep learning.

The paper identifies and characterizes enhancer activity in melanoma cell states using MPRA and DeepMEL2, showing AP-1 drives MES enhancers and SOX/MITF influence MEL enhancers.

The paper uses a deep learning model on melanoma genomes to prioritize enhancer mutations by analyzing allelic imbalance in chromatin accessibility and gene expression.

The paper uses deep learning to analyze enhancer logic across species in melanoma, identifying conserved regulatory complexes and their roles.

The paper spatially integrates single-cell transcriptomics and epigenomics data to identify enhancers and gene regulatory relationships in Drosophila eye-antennal disc.

The paper identifies an intermediate cell state in melanoma through single-cell RNA-seq and ATAC-seq, revealing a gene regulatory network that governs phenotype switching and potential therapeutic targets.