Kai Zhao

INSIDER is a statistical framework for analyzing bulk RNA expression data that models interactions between biological variables and decomposes variation into a shared latent space to improve interpretation and downstream analysis.

This paper uses machine learning to predict disease-specific drug targets by integrating drug-induced expression profiles and gene perturbation data, finding significant enrichment of known targets.

This study uses Mendelian randomization to investigate statin repositioning opportunities and side-effects across 55 traits, finding associations between LDL-C reduction and various outcomes.