MuLeCoG: multi-level contrastive graph network for cancer subtype classification
Yuchun Yang1, Songyang Wu1, Bo Peng1
1School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, People's Republic of China.
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Accurate cancer subtype classification is critical for personalized treatment, yet integrating multi-omics data remains challenging. We present a novel method that constructs multi-level cross-omics graphs and uses GraphSAGE with hierarchical contrastive learning to extract discriminative features for SVM-based classification. Tests on TCGA BRCA and GBM datasets show superior accuracy and reduced computational cost over state-of-the-art methods, enhancing clinical applicability.
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