Guangrong Qin

KGBN enhances logical gene regulatory networks by integrating knowledge graphs and experimental data to create context-aware models for drug response prediction in AML.

This study identifies NF-κB-driven chromatin remodeling as a mechanism for drug-induced dedifferentiation in cancer, revealing its role in promoting drug-tolerant persister states.

This paper characterizes IDH+ glioma stem cell cultures to model GBM biology and therapeutic response, showing reproducible survival curves and methods to track cellular heterogeneity.

The paper describes the transformation of multiomics and EHR data into privacy-preserving biomedical knowledge graphs within the Biomedical Data Translator ecosystem.

LM-Merger merges logical gene regulatory network models to improve coverage and predictive accuracy in disease contexts.

SL-Cloud is a cloud-based resource that facilitates the prediction and comparison of context-specific synthetic lethal interactions using multi-omic datasets.

The paper develops a Functional Module States framework to predict drug sensitivity by analyzing transcriptional states from gene expression data in breast cancer cell lines.