Feb 23, 2024 � This work proposes a method integrating multi-omics data based on supervised graph contrast learning (MCRGCN) to classify cancer subtypes.
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Feb 23, 2024 � This work proposes a method integrating multi-omics data based on supervised graph contrast learning (MCRGCN) to classify cancer subtypes. The�...
This work proposes a method integrating multi-omics data based on supervised graph contrast learning (MCRGCN) to classify cancer subtypes.
Supervised graph contrastive learning for cancer subtype identification through multi-omics data integration.
Jan 3, 2023 � We show that pre-training graph models with a contrastive methodology along with fine-tuning it in a supervised manner is an efficient strategy�...
May 10, 2024 � This perspective provides a focused literature review of research articles in which graph machine learning is utilized for integrated multi-omics data analyses.
MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification � Computer Science, Medicine.
Jan 3, 2023 � Deep learning methods can integrate such data to accurately identify more cancer subtypes. We propose a prognostic model based on a�...
We show that pre-training graph models with a contrastive methodology along with fine-tuning it in a supervised manner is an efficient strategy for multi-omics�...
Aug 18, 2023 � Subtype-DCC proposes an end-to-end multi-omics clustering approach using decoupled contrastive learning to identify cancer subtypes. The�...