Jingxuan Bao

This paper introduces UNITO, a deep learning framework for automated cytometric gating that matches human performance and provides interpretable results without requiring manual input.

The paper uses tiled whole genome sequence data to identify genetic signatures associated with Alzheimer's disease using machine learning methods.

This study identifies sixteen genome-wide significant loci associated with brain age gaps derived from MRI modalities and highlights potential causal relationships between brain aging and diseases.

The paper introduces a homological formalism to analyze higher-order properties of human brain functional sub-circuits and identifies task-specific homological differences in brain connectivity.

The paper uses genetic data to predict imaging-derived phenotypes from the UK Biobank, creating polygenic scores that identify disease risk in external cohorts.

The paper identifies 617 novel genomic loci and pathways associated with structural covariance patterns in the human brain using a stochastic multivariate factorization method.