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Keyword: protein property prediction ×Clear all

2 claims from 2 papers

1. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models

   Li, Amini, Yue, Yang and Lu · bioRxiv (Cold Spring Harbor Laboratory) · 2024

   The authors ran 370 transfer-learning experiments with protein language models and found that, for most downstream tasks, more pretraining does not help, which suggests current pretraining methods fit applications poorly.

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   1. UncheckedAlmost all protein tasks gain from pretrained language models, but most do not improve with more pretraining, relying on low-level features learned early.“We observe that while almost all down-stream tasks do benefit from pretrained models compared to naive sequence representations, for the majority of tasks performance does not scale with pretraining, and instead relies on low-level features learned early in pretraining.”

2. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   Graph with Residue-Based Cross-Modal Framework Enhances Cell Function-Related Protein Properties Prediction

   Gou, Tan, Liu, Li, Fan and Yu · Journal of Chemical Information and Modeling · 2025

   The paper proposes GwR, a framework combining protein language models with two residue graphs, and applies it to four protein property prediction tasks, comparing it with existing models.

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   1. UncheckedThe authors report that their GwR model beats existing methods on both prediction accuracy and training efficiency across four protein property tasks.“Experimental results demonstrate that GwR consistently outperforms existing methods in terms of both predictive performance and training efficiency.”