Breast cancer is one of the most life-threatening and heterogeneous diseases. It contains various molecular subtypes, each subtypes have different characteristics, treatment outcomes, and prognosis. The proper integration of multi-omics data, including genomics, epigenomics, transcriptomics, and proteomics, is very important for enhancing the breast cancer molecular subtypes classification accuracy. Despite the increase in high-dimensional multi-omics data, selecting a suitable integration method for multi-omics data in breast cancer molecular subtypes classification still remains a crucial challenge. This study aims to evaluate and compare, and assess the effectiveness of the multi-omics data integration methods, including exploring the advantages, limitations, and highlighting their performance in terms of accuracy, interpretability, scalability, and biological relevance. Our findings indicate that transformer-based integration methods are increasingly adopted in recent studies due to their superior ability to handle high-dimensional heterogeneous data and capture intricate cross-omics relationships while providing interpretable insights. Additionally, we provide a comparative overview of existing models, discuss key trends over the years, and offer actionable guidance for method selection based on dataset characteristics and research objectives. Finally, we suggest future research directions, emphasizing hybrid deep learning frameworks, graph-based models, and attention mechanisms to enhance predictive accuracy and biological interpretability.
Shah et al. (Thu,) studied this question.