Shan Cong

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.

This study identifies genetic influences on brain connectivity across age groups using GWAS and finds a consistent effect of rs7937515 on network segregation in the left middle temporal gyrus.

The paper develops deep learning models to assess the quality of subcortical brain shape models from MRI, reducing human rater time by 46-70%.

This work uses machine learning to automate quality control of 3D brain shape models, reducing human workload by 30-70% across multiple datasets and diseases.