3 citations · 7 across the 4 of their papers we have counts for
4 papers
Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models
Yang Tan, Mingchen Li, Bingxin Zhou +7
Fine-tuning Pre-trained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised lea…
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning
Ziyi Zhou, Liang Zhang, Yuanxi Yu +3
Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Recently, due to their capacity and representation ability, pre-trained protein l…
PETA: Evaluating the Impact of Protein Transfer Learning with Sub-word Tokenization on Downstream Applications
Yang Tan, Mingchen Li, Pan Tan +4
Large protein language models are adept at capturing the underlying evolutionary information in primary structures, offering significant practical value for protein engineering. Co…
Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks
Bingxin Zhou, Outongyi Lv, Kai Yi +4
Directed evolution as a widely-used engineering strategy faces obstacles in finding desired mutants from the massive size of candidate modifications. While deep learning methods le…