  Due to the high heterogeneity and clinical characteristics of cancer, there
are significant differences in multi-omic data and clinical characteristics
among different cancer subtypes. Therefore, accurate classification of cancer
subtypes can help doctors choose the most appropriate treatment options,
improve treatment outcomes, and provide more accurate patient survival
predictions. In this study, we propose a supervised multi-head attention
mechanism model (SMA) to classify cancer subtypes successfully. The attention
mechanism and feature sharing module of the SMA model can successfully learn
the global and local feature information of multi-omics data. Second, it
enriches the parameters of the model by deeply fusing multi-head attention
encoders from Siamese through the fusion module. Validated by extensive
experiments, the SMA model achieves the highest accuracy, F1 macroscopic, F1
weighted, and accurate classification of cancer subtypes in simulated,
single-cell, and cancer multiomics datasets compared to AE, CNN, and GNN-based
models. Therefore, we contribute to future research on multiomics data using
our attention-based approach.
