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

• URL: http://arxiv.org/abs/2308.10917v1
• Date: Mon, 21 Aug 2023 03:54:21 GMT
• Title: PACS: Prediction and analysis of cancer subtypes from multi-omics data based on a multi-head attention mechanism model
• Authors: Liangrui Pan, Dazheng Liu, Zhichao Feng, Wenjuan Liu, Shaoliang Peng
• Abstract summary: 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. 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.
• Score: 2.275409158519155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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