4 citations · 10 across the 11 of their papers we have counts for
Showing q-bio.BMShow all
3 papers · 1 filter
q-bio.BM2024
CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin Evaluation
Wenrui Gou, Wenhui Ge, Yang Tan +3
Protein structures are important for understanding their functions and interactions. Currently, many protein structure prediction methods are enriching the structure database. Disc…
q-bio.BM2024★ 3 cited
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…
q-bio.BM2023★ 1 cited
Pro-PRIME: A general Temperature-Guided Language model to engineer enhanced Stability and Activity in Proteins
Fan Jiang, Mingchen Li, Jiajun Dong +23
Designing protein mutants of both high stability and activity is a critical yet challenging task in protein engineering. Here, we introduce PRIME, a deep learning model, which can…