Yilun Sheng

This paper introduces SAGE-net, a framework for training sequence-to-function models using personal genomes, showing improved gene expression prediction but limited generalization, while DNA methylation shows better generalization.

GAME provides a standardized framework for benchmarking genomics models across tasks, enabling consistent evaluation and community contributions.

The paper creates a curated MPRA dataset collection and builds sequence-to-function models to analyze how data size, diversity, and model complexity affect predictive performance.