MOAEAM: Multi-omics data integration with autoencoder and attention mechanisms for cancer patient classification and biomarker identification

The integration of multi-omics data is crucial for cancer patient classification and biomarker identification. While this integration presents significant potential, it necessitates the development of sophisticated methodological frameworks. There remains considerable opportunity for en hancement in existing approaches to simultaneously fulfill the demands of omics-specific feature extraction and cross omics association modeling. Consequently, in this study, a deep learning framework based on improved autoencoders and attention mechanism, named MOAEAM, is proposed to address this issue. Specifically, a novel composite loss facilitates the extraction of omics-specific features, and a multi-omics integration module incorporates capture cross omics information, collectively enhancing classification perfor mance. Systematic evaluations across multiple cancer datasets show MOAEAM achieves consistently higher classification performance than current mainstream multi-omics integration methods. Ablation studies reveal that the auxiliary classifier introduced in the improved autoencoder plays a key role in performance improvements. The feature importance scores computed by the model identify potential clinically significant biomarkers, which are further validated through literature analysis and enrichment analysis.