PACS: Prediction and analysis of cancer subtypes from multi-omics data based on a multi-head attention mechanism model
This work addresses the need for precise cancer subtype classification to improve treatment selection and patient survival predictions, representing an incremental advancement in multi-omics data analysis.
The study tackled the problem of accurately classifying cancer subtypes from multi-omics data by proposing a supervised multi-head attention mechanism model (SMA), which achieved the highest accuracy, F1 macroscopic, and F1 weighted scores compared to existing models like AE, CNN, and GNN on simulated, single-cell, and cancer multi-omics datasets.
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.