AET-net: A framework for subtype classification based on the multi-omics data of breast cancer

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Breast cancer (BC) is one of the most prevalent cancers worldwide and remains a significant global public health challenge. The biomechanical characteristics of tumor microenvironments provide critical insights into cellular interactions and mechanical stress responses that potentially influence cancer progression. The integration and analysis of multi-omics data for BC subtype classification present substantial challenges, including high-dimensional data complexity and difficulties in integrating heterogeneous omics data characteristics. To address these challenges, we propose an Autoencoder and Transformer integrated neural network (AET-net) classification framework. The experimental results demonstrate that our model achieves significant performance improvements in predicting BC subtypes based on integrated multi-omics datasets, with an Accuracy of 0.912 and an AUC of 0.9862. These results not only validate the high accuracy of our model in BC subtype classification, providing a valuable tool for diagnostic decision support, but also demonstrate the potential of integrated multi-omics data analysis in enhancing the precision and efficiency of BC subtype identification.