Jian Kang

SPIN integrates spatial omics data for population-scale clinical prediction and biological discovery, achieving high accuracy in dementia and breast cancer outcomes.

This paper introduces a spatial Graphical Regression model to analyze spatial transcriptomics data and identifies novel gene interactions in cancer.

This study identifies 303 novel genetic signals associated with plasma metabolites in Finnish men, revealing biological insights into disease mechanisms.

This study benchmarks integration methods for spatial transcriptomics data, showing that combining highly variable and spatially variable genes improves clustering performance.

The paper introduces a machine learning method to detect brain connectomic biomarkers differentiating Alzheimer's disease and mild cognitive impairment using FDG-PET data.