| dc.description.abstract | Effective understanding of diseases like cancer necessitates integrating diverse information across various physical scales through multimodal data. In this study, we introduce a novel feature embedding module based on canonical correlation analysis (CCA) to capture both intra-modality and inter-modality correlations. Our approach leverages CCA to develop multi-dimensional embeddings that align well across different data sources. We validated our method using both simulated and real datasets, demonstrating its capability to generate well-correlated embeddings. When applied to the one-year survival classification of breast cancer patients from the TCGA-BRCA dataset, our embeddings achieved competitive performance, with average F1 scores reaching up to 58.69% in 5-fold cross-validation. | en_US |
