Gang Qu

This paper introduces a nonlinear mixed model (NMM) for fMRI analysis to better capture nonlinear relationships and individual variability, showing improved model fit compared to linear models.

FADVI integrates single-cell and spatial omics data by disentangling batch effects from biological signals, outperforming existing methods across multiple datasets.

The paper develops M2AE, a multi-task multi-view model to impute blood SCFA levels using gut metagenomic data, dietary features, and host characteristics, showing improved performance over existing methods.

This paper introduces DemoVAE, a variational autoencoder that generates synthetic fMRI data conditioned on demographics and removes demographic confounds from fMRI features.