Abstract: Breast cancer remains a highly heterogeneous disease for which it demands advanced computational techniques that can reveal significant biological patterns in high-dimensional epigenomic data. DNA methylation profiles generated by the Illumina HumanMethylation450 platform yield rich, clinically relevant signals but introduce significant analytical challenges due to their high dimensionality, sparsity, and nonlinear structure. This work presents a novel memory-efficient hybrid learning architecture that combines Truncated Singular Value Decomposition (SVD), a deep Autoencoder, and a multi-model ensemble classifier for boosting subtype classification performance using TCGA-BRCA methylation data. In order to circumvent memory limits and prevent system crashes, a probe-subset extraction strategy combined with variance-based feature selection was employed to ensure fast and safe data loading from the Xena repository. While the autoencoder extracts compact nonlinear manifold representations, SVD captures the global linear variance structure. Further, the fused latent space is modelled by an ensemble including Random Forest, XGBoost, and a lightweight Keras neural classifier that allows the system to exploit different decision limits and achieve robust generalization. The experimental investigation across several architectures demonstrates high predictive performance with ROC-AUC scores exceeding 0.99 and accuracies higher than 0.96 for Basic CNN and MLP models. Furthermore, the proposed hybrid ensemble improves stability and precision by outperforming traditional baselines and confirming the complementary nature of spectral and deep feature extraction. This study is suitable for large-scale biomedical data analytics scenarios. In conclusion, this work provides an efficient hybrid machine learning framework for breast cancer methylation study by offering a strong platform for improved prognostic modelling and development of epigenetic biomarkers.

Hemalatha D and N Gomathi. “HELM-BRCA: Hybrid Embedding and Learning Model for BRCA Methylation Classification”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.1 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170110

@article{D2026,
title = {HELM-BRCA: Hybrid Embedding and Learning Model for BRCA Methylation Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170110},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170110},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {1},
author = {Hemalatha D and N Gomathi}
}