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Found 8 papers, 2 papers with code
Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models
Fine-tuning Pre-trained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised learning approaches.
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning
Accurately modeling the protein fitness landscapes holds great importance for protein engineering.
PETA: Evaluating the Impact of Protein Transfer Learning with Sub-word Tokenization on Downstream Applications
Moreover, despite the wealth of benchmarks and studies in the natural language community, there remains a lack of a comprehensive benchmark for systematically evaluating protein language model quality.
A general Temperature-Guided Language model to engineer enhanced Stability and Activity in Proteins
Designing protein mutants with high stability and activity is a critical yet challenging task in protein engineering.
Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks
Directed evolution as a widely-used engineering strategy faces obstacles in finding desired mutants from the massive size of candidate modifications.
TemPL: A Novel Deep Learning Model for Zero-Shot Prediction of Protein Stability and Activity Based on Temperature-Guided Language Modeling
We introduce TemPL, a novel deep learning approach for zero-shot prediction of protein stability and activity, harnessing temperature-guided language modeling.
SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering
Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism.
InterFace:Adjustable Angular Margin Inter-class Loss for Deep Face Recognition
In the field of face recognition, it is always a hot research topic to improve the loss solution to make the face features extracted by the network have greater discriminative power.
