Predicting DNA damage response using synthetic cell painting profiles and experimental analysis

TL;DR

This paper introduces a machine learning framework using synthetic cell painting data to accurately predict DNA damage response, validated through experimental testing.

Contribution

A novel DDR prediction framework using synthetic data augmentation with Gaussian copula and validated through γH2AX and viability assays.

Findings

SVM trained on real data augmented with Gaussian copula synthetic data achieved F1-score of 0.87 and AUROC of 0.94.

Gaussian copula outperformed CTGAN, VAE, and CopulaGAN in preserving morphological fidelity.

Model identified DDR inducers confirmed by γH2AX accumulation and reduced cell viability in external dataset.

Abstract

Detecting DNA damage response (DDR) using cell painting profiles is challenging due to limited sample sizes and skewed class distributions. We established a robust classification framework to enhance DDR prediction based on synthetic data. Using the idr-0080 dataset, we generated synthetic profiles with the Gaussian copula, CTGAN, VAE, and CopulaGAN algorithms, and assessed their quality through fidelity metrics. Among four classifiers evaluated with real and/or synthetic data under preserved or resolved class imbalance, an SVM trained on real data augmented by Gaussian copula-generated synthetic data achieved the best performance (F1-score = 0.87, AUROC = 0.94). SHAP analysis highlighted key predictive morphological features. The model successfully identified known and previously unreported DDR inducers in the external cpg-0012 dataset, which were experimentally confirmed by γH2AX marker accumulation and reduced cell viability. Overall, our machine learning framework integrating synthetic cell painting profiles effectively predicted DDR, providing a scalable virtual prescreening approach for drug discovery.

•Synthetic Cell Painting profiles improve DNA damage response (DDR) prediction•Gaussian Copula data best preserves morphological fidelity among synthetic generators•SVM with synthetic augmentation achieves high DDR classification accuracy•Predicted DDR inducers are validated by γH2AX induction and cell viability assays

Synthetic Cell Painting profiles improve DNA damage response (DDR) prediction

Gaussian Copula data best preserves morphological fidelity among synthetic generators

SVM with synthetic augmentation achieves high DDR classification accuracy

Predicted DDR inducers are validated by γH2AX induction and cell viability assays

Systems biology; Data processing in systems biology; Machine learning

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