Yong He

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.