Yiyang Yu

This study evaluates pre-trained DNA language models' ability to predict and interpret cell-type-specific functional genomics data, finding they do not outperform conventional machine learning approaches.

The paper trains sparse autoencoders on a protein language model to identify features that represent protein sequences, revealing both generic and family-specific patterns and their potential biological roles.

This paper introduces D3, a generative model for designing regulatory DNA with tunable activity, showing improved performance over baselines in sequence generation and activity prediction.

This paper extends EvoAug, an evolution-inspired data augmentation method for genomic deep learning, to TensorFlow, demonstrating comparable performance to the original PyTorch-based version.