Lei Xie

The paper develops an interpretable deep learning model to improve CRISPR-Cas9 sgRNA prediction by incorporating gene network context.

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.

The paper develops a multi-rank, multi-layered recommender system, ANTENNA, to predict drug-gene-disease associations and repurposes diazoxide as a potential anti-cancer therapy for triple-negative breast cancer.

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

The paper maps FDA drugs to Plasmodium falciparum targets to identify potential new anti-malaria candidates and multi-target therapeutics.