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Due to the high heterogeneity and clinical characteristics of cancer, thereare significant differences in multi-omic data and clinical characteristicsamong different cancer subtypes. Therefore, accurate classification of cancersubtypes can help doctors choose the most appropriate treatment options,improve treatment outcomes, and provide more accurate patient survivalpredictions. In this study, we propose a supervised multi-head attentionmechanism model (SMA) to classify cancer subtypes successfully. The attentionmechanism and feature sharing module of the SMA model can successfully learnthe global and local feature information of multi-omics data. Second, itenriches the parameters of the model by deeply fusing multi-head attentionencoders from Siamese through the fusion module. Validated by extensiveexperiments, the SMA model achieves the highest accuracy, F1 macroscopic, F1weighted, and accurate classification of cancer subtypes in simulated,single-cell, and cancer multiomics datasets compared to AE, CNN, and GNN-basedmodels. Therefore, we contribute to future research on multiomics data usingour attention-based approach.

PACS: Prediction and analysis of cancer subtypes from multi-omics data based on a multi-head attention mechanism model