Yuanxi Yu

VenusGenome improves microbial genome representation learning by using a genome-aware masked autoencoder, achieving high performance in phenotype prediction and gene-level tasks.

MAXWELL calibrates protein language models to predict mutation-induced stability changes, outperforming existing methods and successfully designing stabilizing mutations in DhaA dehalogenase.

This study introduces Venus-TIGER, a deep learning model that identifies critical amino acids for protein expression, showing improved accuracy and transferability across datasets.

The paper introduces Venus-MAXWELL, a framework for predicting protein mutant stability using sequence-to-landscape learning, achieving higher accuracy and faster inference than existing methods.

This paper introduces ProPEC, a perplexity-based ensemble model for zero-shot prediction of viral protein fitness, showing improved performance over individual models.