This invention provides a method and
system for
cancer subtype identification based on multi-
omics data, belonging to the field of
bioinformatics processing technology. The method includes: constructing a DILCORE model that integrates a multi-
branch variational
autoencoder, contrastive learning, and cross-view attention mechanisms. The multi-
branch variational
autoencoder decomposes
omics observation data into common components for
subtype classification and view-specific
noise components, achieving
noise suppression; the InfoNCE contrastive loss is introduced to bring common representations of the same sample closer across different views, enhancing cross-
omics consistency; the cross-view residual self-attention mechanism is used to adaptively weight and fuse common vectors; finally, a self-
supervised clustering fine-tuning optimization strategy is introduced to jointly improve representation quality and clustering performance in the latent space. This achieves deep and effective integration of multi-
omics data, significantly improving the accuracy of
cancer subtype identification and providing a powerful tool for personalized
cancer treatment and prognostic assessment.