Jian Kang

Population-Scale Integration of Spatial Omics Networks for Clinical Prediction and Biological Discovery by SPIN

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

Spatially varying graph estimation for spatial transcriptomics cancer data

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

Genome-wide association study of 1,391 plasma metabolites in 6,136 Finnish men identifies 303 novel signals and provides biological insights into human diseases

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

Benchmarking Computational Integration Methods for Spatial Transcriptomics Data

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

A machine-learning approach for detection of local brain networks and marginally weak signals identifies novel AD/MCI differentiating connectomic neuroimaging biomarkers

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