Yuxin Shen

The paper uses machine learning to predict membrane protein expression from sequence data, achieving high accuracy and guiding protein engineering improvements.

The paper develops a method to estimate enzymatic activities from genetic variants using fitness assays and combines allele effects to predict pathogenicity in recessive diseases.

This study improves sequence-to-expression models by integrating mechanistic sequence features with one-hot encodings, enhancing generalization across Escherichia coli and Saccharomyces cerevisiae datasets.

This paper demonstrates that active learning improves regulatory DNA sequence optimization for yeast promoters, outperforming one-shot methods in complex landscapes.