MoACNN-XGNet: Interpretable Multi-Omics Convolutional Network for Breast Cancer Subtyping and Prognostic Genes Identification.

Breast cancer, a highly heterogeneous disease at both the phenotypic and molecular levels, presents significant challenges for prognosis and treatment. Accurate subtyping of breast cancer is critical due to its complex biological characteristics, which directly influence disease progression and therapeutic outcomes. In this study, we integrate multi-omics data, including copy number variation, RNA sequencing, and DNA methylation, to generate two-dimensional representations of each sample using Uniform Manifold Approximation and Projection. This transformation enhances data interpretability and supports subsequent learning tasks. Traditional convolutional neural networks have demonstrated potential in medical image analysis but often struggle with high-dimensional omics data. To address this limitation, we propose MoACNN-XGNet, an attention-based convolutional neural network framework that prioritizes key features within image-transformed multi-omics data. Our method significantly improves the precision of subtype classification and effectively overcomes the challenges posed by the high dimensionality and structural complexity of multi-omics data. Furthermore, we employ the Guided Grad-CAM method to enhance model interpretability, enabling the identification of subtype-specific explainable genes. Subsequent enrichment and survival analyses of these genes reveal critical biological pathways and potential therapeutic targets. This study offers a novel approach to refining breast cancer subtyping and highlights the potential for personalized treatment strategies, ultimately aiming to improve patient survival outcomes.