Yuqing Zhang

This paper develops an ensemble learning approach to improve genomic classifiers by integrating predictions across batches rather than removing batch effects from merged data, showing better performance in independent validation.

This study investigates how different sources of heterogeneity affect the accuracy of genomic prediction models across studies, finding that some sources have limited impact while others, like identical generative models, significantly improve consistency.