MO-GCAN: multi-omics integration based on graph convolutional and attention networks.

ABSTRACT:

Motivation

Cancer subtypes play a critical role in disease progression, prognosis, and treatment, making their detection essential for tailoring precision medicine. Studies have shown that multi-omics integration outperforms single-omics approaches in cancer subtyping tasks. However, due to the high-dimensionality of multi-omics data, many existing studies either fail to capture the correlation between true labels and learned features, or lack sufficient capacity to model complex biological representations. These limitations hinder the full potential of leveraging the rich and complementary information embedded in multi-omics datasets.

Result

We propose a framework that leverages supervised feature learning and classification based on a graph-based learning approach with att

SUBMITTER: Dou Y 

PROVIDER: S-EPMC12342174 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature