Jiadong Ji

The paper develops a cross-ancestry information transfer framework to improve protein abundance prediction and protein-trait association identification in underrepresented populations.

omicsMIC benchmarks 28 imputation methods for mass spectrometry-based omics data, enabling researchers to evaluate and select methods based on real-time visualizations.

The paper introduces a semi-parametric latent differential network model for analyzing genetic networks across conditions, showing it works well for various data types and provides deeper insights into disease mechanisms.

The paper introduces a Matrix-Variate Differential Network model to detect brain connectivity differences between groups and applies it to EEG data to identify differential interactions.

JDINAC identifies differential interaction networks and classifies diseases using high-dimensional sparse omics data without parametric assumptions, achieving accurate results in breast cancer analysis.