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.

我们提出了一种监督式多头注意机制模型（SMA）来成功分类癌症亚型。SMA 模型的注意机制和特征共享模块可以成功学习多组学数据的全局和局部特征信息。通过与 AE、CNN 和 GNN 等模型在模拟、单细胞和癌症多组学数据集上进行广泛实验验证，SMA 模型相比其他模型在准确性、F1 宏观值、F1 加权值和癌症亚型分类上取得了最高的精度，因此为基于多组学数据的未来研究做出了贡献。