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Related Experiment Video

Updated: Jun 14, 2026

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

scGSDR: Harnessing gene semantics for single-cell pharmacological profiling.

Yu-An Huang^1,2, Xiyue Cao², Zhu-Hong You³

¹Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China.

Communications Biology

|October 30, 2025

Summary

This summary is machine-generated.

We developed scGSDR, a computational model that predicts cellular responses to drugs by integrating gene semantics and pathways. This tool enhances precision medicine by identifying drug resistance mechanisms and aiding targeted therapy development.

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Area of Science:

Computational Biology
Pharmacogenomics
Precision Medicine

Background:

Single-cell sequencing reveals cellular heterogeneity's role in drug resistance.
Computational models can predict cellular responses to drugs using existing data.
Understanding gene semantics and signaling pathways is crucial for biological insights.

Purpose of the Study:

To develop a computational model (scGSDR) for predicting cellular responses to drugs.
To enhance predictive performance by incorporating gene semantics and pathway information.
To identify key pathways and genes contributing to drug resistance phenotypes.

Main Methods:

Integrated two computational pipelines focusing on cellular states and gene signaling pathways.
Incorporated gene semantics into the model for improved predictive accuracy.
Developed an interpretability module to identify resistance-related pathways and genes.

Main Results:

scGSDR demonstrated superior predictive accuracy compared to existing methods using bulk or single-cell RNA sequencing data.
The model successfully predicted responses to single drugs and drug combinations.
Biological interpretability of attention scores identified relevant genes (e.g., BCL2, CCND1, PIK3CA) and pathways involved in drug resistance.

Conclusions:

scGSDR effectively models cellular responses to diverse drugs, including combinations, by incorporating gene semantics.
The model aids in identifying key drug resistance pathways, advancing precision medicine.
scGSDR facilitates targeted therapy development by pinpointing potential drug-related genes.