# DiffRS-net: A Novel Framework for Classifying Breast Cancer Subtypes on Multi-Omics Data
###### Abstract  
The precise classification of breast cancer subtypes is crucial for clinical diagnosis and treatment, yet early symptoms are often subtle. The use of multi-omics data from high-throughput sequencing can improve the classification accuracy. However, most research primarily focuses on the association between individual omics data and breast cancer, neglecting the interactions between different omics. This may fail to provide a comprehensive understanding of the biological processes 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 performs a differential analysis on each omics datum to identify differentially expressed genes (DE-genes) and adopts a robustness-aware Sparse Multi-View Canonical Correlation Analysis to detect multi-way association among DE-genes. These DE-genes with high levels of correlation are then used to train an attention learning network, thereby enhancing the prediction accuracy of breast cancer subtypes. The experimental results show that, by mining the associations between multi-omics data, DiffRS-net achieves a more accurate classification of breast cancer subtypes than the existing methods. breast cancer subtypes; multi-omics data integration; Sparse Multi-View Canonical Correlation Analysis; attention learning network +
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forest model using gene expression and DNA methylation data to classify breast cancer subtypes. However, both concatenation-based and ensemble-based approaches fail to elucidate the intrinsic relationships between different types of data, limiting our understanding of the interactions between various biological processes [20; 21]. Subsequently, researchers began to integrate multi-omics data based on prior knowledge to enhance the accuracy of breast cancer subtype prediction. For instance, [PERSON] et al. [22] developed a method called DIABLO to select a subset of features to find shared information across different data modalities, aiming to discriminate multiple breast cancer subtypes simultaneously. [PERSON] et al. [23] presented SMSPL, a method that employs a novel soft weighting scheme to interactively recommend samples with high confidence to classify breast cancer subtypes. However, the prior knowledge might not be comprehensive or precise, which could lead to incorrect diagnoses [22]. Recently, deep learning-based methods for predicting breast cancer subtype based on multi-omics data have achieved great advance [24; 25; 26]. [PERSON] et al. [27] introduced a deep learning model called DeepMo for classifying breast cancer subtypes using multi-omics data. DeepMo applies fully connected layers to each omics type and integrates these layers to form a comprehensive network to identify breast cancer subtypes. MOGONET [28] is a tumor grade classification model. It conducts joint training to learn the correlation information of different omics to perform tumor grade classification. Although these frameworks have gained notable success in breast subtype classification, there are still limitations. First, one major challenge that biological samples commonly encounter is the limit number of samples coupled with high-dimensional features, posing a significant difficulty for neural network models that rely on abundant sample data to optimize performance. From a biological perspective, typically, only a very small number of features are closely associated with a specific disease. From a machine learning perspective, an abundance of features can lead to overfitting, diminishing the effectiveness of classifiers and adversely affecting their accuracy [29]. Second, the training process of deep learning models is not sufficiently transparent, making interpretative analysis challenging. Identifying key predictive features within each omics dataset is also a daunting task [30].