Dajiang J. Liu

A generalized supervised contrastive learning framework for integrative multi-omics prediction models

MB-SupCon-cont improves prediction accuracy by incorporating a generalized contrastive loss function that defines similarity and dissimilarity for continuous responses using three distance-based weighting methods, and provides superior representation learning and improves data visualization in lower-dimensional spaces.