  Due to the high heterogeneity and clinical characteristics of cancer, there
are significant differences in multi-omics data and clinical features among
subtypes of different cancers. Therefore, the identification and discovery of
cancer subtypes are crucial for the diagnosis, treatment, and prognosis of
cancer. In this study, we proposed a generalization framework based on
attention mechanisms for unsupervised contrastive learning to analyze cancer
multi-omics data for the identification and characterization of cancer
subtypes. The framework contains a symmetric unsupervised multi-head attention
encoder, which can deeply extract contextual features and long-range
dependencies of multi-omics data, reducing the impact of noise in multi-omics
data. Importantly, the proposed framework includes a decoupled contrastive
learning model (DEDUCE) based on a multi-head attention mechanism to learn
multi-omics data features and clustering and identify cancer subtypes. This
method clusters subtypes by calculating the similarity between samples in the
feature space and sample space of multi-omics data. The basic idea is to
decouple different attributes of multi-omics data features and learn them as
contrasting terms. Construct a contrastive loss function to measure the
difference between positive examples and negative examples, and minimize this
difference, thereby encouraging the model to learn better feature
representation. The DEDUCE model conducts large-scale experiments on simulated
multi-omics data sets, single-cell multi-omics data sets and cancer multi-omics
data sets, and the results are better than 10 deep learning models. Finally, we
used the DEDUCE model to reveal six cancer subtypes of AML. By analyzing GO
functional enrichment, subtype-specific biological functions and GSEA of AML,
