A hierarchical graph network that fuses patient-specific PPI graphs with a clinical patient-similarity graph improves atherosclerosis subtype classification and suggests two molecular clusters per imaging subtype, but the evaluation protocol raises leakage concerns.
Graph Contrastive Learning for Multi-omics Data
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abstract
Advancements in technologies related to working with omics data require novel computation methods to fully leverage information and help develop a better understanding of human diseases. This paper studies the effects of introducing graph contrastive learning to help leverage graph structure and information to produce better representations for downstream classification tasks for multi-omics datasets. We present a learnining framework named Multi-Omics Graph Contrastive Learner(MOGCL) which outperforms several aproaches for integrating multi-omics data for supervised learning tasks. 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 data classification.
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Atherosclerosis through Hierarchical Explainable Neural Network Analysis
A hierarchical graph network that fuses patient-specific PPI graphs with a clinical patient-similarity graph improves atherosclerosis subtype classification and suggests two molecular clusters per imaging subtype, but the evaluation protocol raises leakage concerns.