Canzhuang Sun

Abstract Sequence-based deep learning has advanced genome interpretation, yet most models remain task-specific and rely on retraining, limiting scalability across biological contexts. Here we present SUCCEED, a supervised multi-task DNA foundation model pretrained on 6,389 ENCODE functional genomics tracks to learn transferable regulatory representations.

Abstract The development of sequence-based deep learning methods has greatly increased our understanding of how sequence determines function. In parallel, numerous interpretable algorithms have been developed to address complex tasks, such as elucidating sequence regulatory syntax and analyzing non-coding variants from trained models. However, few studies have systematically compared and evaluated the performance and interpretability of these algorithms.