Qiao Liu

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.

The paper introduces CODE-AE, a context-aware deconfounding autoencoder, to predict patient-specific drug responses from in vitro screens and discovers personalized therapies and biomarkers.

TranSynergy improves synergistic drug combination prediction by integrating biological knowledge and enhancing interpretability, revealing novel pathways and predicting new combinations for ovarian cancer.

This paper constructs a disease-centric microbiome network and identifies potential drug targets to modulate the microbiome ecosystem, showing diabetes drugs may be repurposed for microbiome modulation.

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