Daniel X. Quang

The paper uses single-cell ATAC-seq and deep learning to analyze pancreatic islet cells, revealing cell-specific regulatory signatures linked to type 2 diabetes.

FactorNet predicts cell type-specific TF binding from sequential data using a convolutional-recurrent neural network, improving efficiency and interpretability in gene regulation studies.

YAMDA accelerates EM-based motif discovery by using PyTorch and GPU, achieving thousandfold speedup while maintaining MEME-level accuracy.

The paper introduces DeepCons, a deep learning model to predict evolutionary conservation in DNA sequences and identifies both known and novel functional elements.