Hong Liang

GREMI improves multi-omics disease prediction by integrating biomolecular interactions and provides interpretable module identification.

MOCAT improves multi-omics data integration using auxiliary classifiers and attention mechanisms, achieving better classification and biomarker discovery than existing methods.

TEMINET improves diagnostic prediction by integrating multi-omics data through co-informative representations and trustworthy learning strategies, showing better performance across four diseases.