# DiffRS-net: A Novel Framework for Classifying Breast Cancer Subtypes on Multi-Omics Data
## 4 Conclusions  
In this work, we present an attention learning framework called DiffRS-net to classify breast cancer subtypes using multi-omics data. DiffRS-net first performs a differential  
Figure 6: Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the top 500 mRNAs with the highest weights.  
analysis on the gene expression data of the healthy and disease groups to detect DE-genes among multi-omics data. This procedure can effectively reduce the dimensionality of omics data by filtering out features with low expression levels, and it extracts potential biomarkers, which could be crucial for understanding the biological mechanisms of breast cancer. Subsequently, DiffRS-net employs a robustness-aware adaptive SMCCA algorithm on DE-genes to identify the multi-way association among the different omics, thereby identifying DE-genes with high correlation. Finally, DiffRS-net uses an attention learning network model to train these highly correlated genes and builds a dot-product attention mechanism into the neural network model to allow each task to focus only on its own features and to integrate the relevant information of other tasks. We evaluate the performance of DiffRS-net by comparing it with the state-of-the-art techniques on the binary and multi-classification of breast cancer subtypes. The experimental results show the superiority of our approach. The ablation study further reveals that each step in DiffRS-net significantly contributes to the improvement of the classification performance. Consequently, DSCCN can be regarded as a valuable framework for the classification of breast cancer subtypes.  
Despite the effectiveness of DiffRS-net in classifying breast cancer subtypes, limitations remain. Initially, due to a data imbalance in the breast cancer sample set, our model is unable to adequately learn the characteristics of samples, which lead to a decrease in accuracy. Considering that the data augmentation technique has proven effective in many fields, we intend to incorporate data augmentation into our future work to enable our model to fully recognize the features of each subtype. Second, we believe that including a wider variety of omics features could contribute to increasing the precision of our model. We aim to incorporate a richer array of omics data, such as variant copy numbers and phenotypes, to expand the model's feature set. Last, incorporating external datasets for validation will further enhance the reliability of our model. In our future work, we intend to seek datasets beyond TCGA for independent verification.  
Conceptualization, P.Z.; Methodology, P.Z.; Software, P.Z.; Formal analysis, Y.H.; Writing--original draft, P.Z.; Writing--review & editing, C.H. and Y.H.; Visualization, C.H. and Y.H.; Supervision, C.H.; Funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Natural Science Foundation of Guangxi Province (No. 2020 GXNSFAA159074) and the National Natural Science Foundation of China (No. 61862006 and No. 62261003).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare on conflicts of interest.