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GPO-VAE: modeling explainable gene perturbation responses utilizing GRN-aligned parameter optimization.

Seungheun Baek^1,2, Soyon Park¹, Yan Ting Chok¹

¹Department of Computer Science and Engineering, Korea University, Seoul, 02841, South Korea.

Bioinformatics (Oxford, England)

|July 15, 2025

Summary

This summary is machine-generated.

We developed GPO-VAE, a novel variational autoencoder (VAE) that integrates gene regulatory networks (GRNs) for explainable prediction of cellular responses to genetic perturbations. This approach enhances biological AI interpretability and achieves state-of-the-art performance.

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Area of Science:

Computational Biology
Systems Biology
Artificial Intelligence in Biology

Background:

Predicting cellular responses to genetic perturbations is crucial for biological understanding and therapeutics.
Variational autoencoders (VAEs) show potential but lack biological explainability.
Gene regulatory networks (GRNs) offer a pathway to explain biological AI models.

Purpose of the Study:

To develop an explainable VAE model for predicting cellular responses to genetic perturbations.
To enhance the interpretability of deep learning models in biological AI by integrating GRNs.
To achieve state-of-the-art performance in perturbation prediction while providing biologically meaningful explanations.

Main Methods:

Proposed GPO-VAE, a VAE model incorporating GRN-aligned Parameter Optimization.
Explicitly modeled gene regulatory networks within the VAE's latent space.
Optimized model parameters for GRN-aligned explainability of latent perturbation effects.

Main Results:

GPO-VAE achieved state-of-the-art performance in predicting transcriptional responses across benchmark datasets.
The model demonstrated strong performance in GRN inference, generating meaningful networks.
Qualitative analysis confirmed GPO-VAE's ability to construct biologically plausible GRNs aligned with known pathways.

Conclusions:

GPO-VAE successfully integrates GRNs into VAEs for explainable perturbation response prediction.
The model offers a significant advancement in biological AI interpretability.
GPO-VAE provides a powerful tool for understanding genetic perturbations and designing targeted therapies.