Ethan C. Alley

This paper introduces a data-efficient deep learning approach for protein engineering that uses minimal experimental data to predict and optimize protein function with high throughput.

This paper develops a machine learning toolkit to attribute genetic engineering to labs by analyzing DNA motifs and phenotype, achieving 70% accuracy in distinguishing over 1,300 labs.

This paper introduces UniRep, a deep learning-based representation of protein sequences that enables accurate prediction of protein stability and function while reducing engineering costs.