Yan Gao

The paper introduces ITN, a deep learning model that improves subgroup-specific prediction by integrating multiple outcomes and subgroups in biomedical datasets.

This paper uses transfer learning to improve disease prediction accuracy for underrepresented populations without harming accuracy for others.

This paper improves time-to-event prediction by integrating compatible related clinical outcomes, enhancing performance without requiring auxiliary data during prediction.

This study shows transfer learning can reduce health care disparities by improving model performance for underrepresented racial groups in cancer omics data.