The precise classification of breast cancer subtypes is crucial for clinical diagnosis and treatment, yet early symptoms are often subtle. Utilizing multi-omics data from high-throughput sequenc-ing can improve the classification accuracy. However, most research primarily focus on the as-sociation between individual omics data and breast cancer, neglecting the interactions between different omics. This may fail to provide a comprehensive understanding of the biological pro-cesses of breast cancer. Here we propose a novel framework called DiffRS-net for classifying breast cancer subtypes by identifying the association among different omics. DiffRS-net per-forms differential analysis on each omics data to identify differentially expressed genes (DE-genes) and adopts a robustness-aware sparse typical correlation analysis to detect multi-way association among DE genes. These DE-genes with high levels of correlation are then used to train a multi-task neural network, thereby enhancing the prediction accuracy of breast cancer subtypes. Experimental results show that by mining the associations between multi-omics data, DiffRS-net achieves more accurate classification of breast cancer subtypes than the existing methods.
Run "Running.m" to get the result of the Multi-way association analysis of the mult-omics data.
"rAdaSMCCA.m" is the function of robustness-aware adaptive SMCCA model.
"normalize.m" is the function to normalize the input data.
"updataD.m" is the function to iterate the weights.
Run "binaryC.py" to get the performance of binary classification of DiffRS-net.
Run "multiC.py" to get the performance of multi-classification of DiffRS-net.
Contact: If you have any questions or suggestions with the code, please let us know. Contact Yiran Huang at hyr@gxu.edu.cn
title = {Multi-Task Learning Framework for Classifying Breast Cancer Subtypes on Multi-Omics data)