Xiaoyu Duan

The paper introduces MESIC, a method that maps gene expression data into an interpretable semantic space using gene annotations, enabling biological interpretation of clusters and outliers.

The paper develops a hybrid deep learning-mechanistic model to infer gene regulatory dynamics from a spatiotemporal single-cell atlas of Drosophila embryos, enabling interpretable predictions of gene interactions.

This paper develops a deep learning framework for parameter inference in physiological models using simulated data and clinical glucose tolerance testing data.