Hybrid GNN-Transformer model for multi-omic cancer classification with interpretable pathway-driven feature selection

Accurate classification of cancer subtypes is critical for precision oncology, yet existing methods often fail to integrate multi-omic data while providing biologically interpretable insights. This study presents a novel hybrid deep learning framework for cancer classification that synergistically combines graph neural networks (GNNs) and transformers to model complex interactions across genomic, epigenetic, and functional biological layers. Our approach introduces three key innovations: (1) a heterogeneous graph integrating gene expression, DNA methylation, and pathway nodes with biologically meaningful edges (protein interactions, regulatory relationships); (2) discrete wavelet transforms for spatial-aware dimensionality reduction of methylation data, preserving critical regional patterns; and (3) pathway-guided attention mechanisms that prioritize oncogenic signaling pathways while ensuring interpretability. Experiments demonstrate superior performance over state-of-the-art methods in the classification of 23 cancer types and of breast-cancer subtypes. By bridging artificial intelligence (AI) with cancer biology, our framework enables interpretable multi-omic integration, offering clinicians pathway-level explanations for predictions while maintaining cross-platform robustness. This work advances precision oncology by providing both accurate classification and actionable biological insights, paving the way for improved therapeutic strategies.