A Generalized Supervised Contrastive Learning Framework for Integrative Multi-omics Prediction Models
Yang, S.; Wang, S.; Wang, Y.; Rong, R.; Li, B.; Koh, A. I.; Xiao, G.; Liu, D.; Zhan, X.
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Recent technological advances have highlighted the significant impact of the human microbiome and metabolites on physiological conditions. Integrating microbiome and metabolite data has shown promise in predictive capabilities. We developed a new supervised contrastive learning framework, MB-SupCon-cont, that (1) proposes a general contrastive learning framework for continuous outcomes and (2) improves prediction accuracy over models using single omics data. Simulation studies confirmed the improved performance of MB-SupCon-cont, and applied scenarios in type 2 diabetes and high-fat diet studies also showed improved prediction performance. Overall, MB-SupCon-cont is a versatile research tool for multi-omics prediction models.
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