COAT: A Cross-Omics Attention Transformer for PAM50 Breast Cancer Subtype Classification

Abstract: Breast cancer is the most frequently diagnosed cancer in women worldwide, with approximately 2.3 million new cases annually. Accurate molecular subtyping is essential for guiding treatment decisions; however, existing PAM50 classifiers rely solely on mRNA expression and remain susceptible to normalization artifacts and platform-specific biases. To overcome these limitations, we propose COAT (Cross-Omics Attention Transformer), a novel deep learning framework that integrates mRNA, miRNA, and DNA methylation data to robustly classify PAM50 breast cancer subtypes. The model projects each omics modality into a shared latent space using modality-specific multilayer perceptrons and leverages a directed inter-omics attention mechanism to capture complementary interactions across modalities. The merged representations are processed by a classification head trained with class-weighted cross-entropy to correct for class imbalance. The model was evaluated on the TCGA-BRCA dataset (824 PAM50-tagged samples) using 5-fold stratified cross-validation, achieving an accuracy of 0.822 ± 0.020, a macro F1 score of 0.817 ± 0.033, and a macro area under the ROC curve (AUC) of 0.954 ± 0.011. These results demonstrate high performance compared to mono-omics approaches and traditional machine learning methods, while remaining competitive with recent multi-omics models. An additional 10-fold cross-validation experiment with Bayesian hyperparameter optimization further improved performance (accuracy = 0.852 ± 0.030, macro F1 score = 0.836 ± 0.041, macro AUC-ROC = 0.957 ± 0.013), indicating stable performance across different validation conditions. GradientSHAP interpretability analysis revealed that COAT identified biologically relevant biomarkers, including ERBB2 and GRB7 for the HER2-enriched subtype, ESR1 and PGR for the Luminal A subtype, and KRT5 and FOXM1 for the Basal subtype. Overall, COAT demonstrates that directed inter-omics cross-attention effectively integrates complementary multi-omics signals, achieving strong predictive performance while preserving biological interpretability and providing a generalizable framework for multi-omics-based cancer classification.

Soufiane El Atfa, Abdelmajid Hajami, Hamid Machhour and Hakim Allali. “COAT: A Cross-Omics Attention Transformer for PAM50 Breast Cancer Subtype Classification”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.5 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170537

@article{Atfa2026,
title = {COAT: A Cross-Omics Attention Transformer for PAM50 Breast Cancer Subtype Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170537},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170537},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {5},
author = {Soufiane El Atfa and Abdelmajid Hajami and Hamid Machhour and Hakim Allali}
}