Yue Qiu

The paper introduces a deep learning framework, CIGER, for predicting gene expression rankings induced by chemicals and applies it to pancreatic cancer drug repurposing, showing improved performance over existing methods.

This paper introduces MultiDCP, a deep learning model for multiplex chemical phenomics to screen compounds for novel cells and patients, showing improved performance in drug repurposing for Alzheimer's disease.

This paper introduces DeepCE, a deep learning framework for chemical-induced gene expression prediction and compound screening, showing improved performance over existing methods and demonstrating its application to COVID-19 drug repurposing.

This paper introduces DISAE, a deep learning framework that predicts ligands for unannotated proteins and applies it to assign ligands to human orphan GPCRs.

This paper introduces DISAE, a protein pre-training method for deorphanization, which improves generalizability and outperforms existing methods in predicting small molecule binding to orphan proteins.

This study identifies R47H-TREM2 mutation-induced proinflammatory microglial states in human AD and tauopathy mice, showing Akt pathway involvement and potential therapeutic targets.

The paper develops a Bayesian method to improve gene expression signal detection in LINCS L1000 data, showing better reliability and informativeness than existing approaches.

The paper develops a multi-target screening platform to discover dual-action drugs for cancer therapy, identifying levosimendan as a potential candidate that reduces heart failure risk and inhibits cancer growth.

This paper introduces CRISPR-TRiM, a method for tissue-specific gene knockout in Drosophila, showing it can reveal redundant gene functions in neuronal development.