Francesca-Zhoufan Li

LevSeq rapidly generates sequence-function data for protein engineering using nanopore sequencing and dual barcoding, enabling directed evolution and machine learning applications.

This study evaluates machine learning-assisted directed evolution strategies across diverse protein fitness landscapes, finding that focused training with zero-shot predictors outperforms random sampling in challenging landscapes.

This study analyzes transfer learning in protein language models, finding that downstream task performance often relies on early pretraining features rather than scaling with pretraining duration.