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GEMDiff: a diffusion workflow bridges between normal and tumor gene expression states: a breast cancer case study.

Xusheng Ai¹, Melissa C Smith¹, F Alex Feltus^2,3,4

¹Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634, United States.

Briefings in Bioinformatics

|March 11, 2025

Summary

GEMDiff, a novel computational workflow, enhances breast cancer research by simulating gene expression changes between normal and tumor states. This approach aids biomarker discovery and improves machine learning model performance using RNA-seq data.

Keywords:

diffusion model gene state transition generative model genetic subsystem discovery genotype–phenotype interaction

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Area of Science:

Computational Biology
Genomics
Machine Learning

Background:

Breast cancer complexity stems from genetic/epigenetic mutations, challenging traditional machine learning for drug design.
Existing generative models struggle with scalability and stability for large-scale gene expression data.

Purpose of the Study:

Introduce GEMDiff, a diffusion model-based workflow to bridge normal and tumor gene expression states.
Enhance biomarker identification and improve machine learning model performance through data augmentation and perturbation simulation.

Main Methods:

Utilized a diffusion model (GEMDiff) to augment RNA-seq data and simulate gene expression perturbations.
Developed a scalable and stable workflow for analyzing large-scale gene expression datasets.
Avoided task-specific hyper-parameter tuning and specialized loss functions for broad applicability.

Main Results:

GEMDiff effectively handles large-scale gene expression data without scalability or stability issues.
Demonstrated utility in a breast cancer case study, identifying 307 core genes in tumor-to-normal state transitions.
Generated synthetic data to address limitations in biological data availability for machine learning.

Conclusions:

GEMDiff offers a robust and generalizable tool for gene expression analysis and biomarker discovery.
The workflow facilitates the development of improved predictive models by augmenting biological data.
Open-source availability promotes wider adoption and advancement in computational cancer research.