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Structure-Informed Protein Language Models are Robust Predictors for Variant Effects
Predicting protein variant effects through machine learning is often challenged by the scarcity of experimentally measured effect labels. Recently, protein language models (pLMs) emerge as zero-shot predictors without the need of effect labels, by modeling the evolutionary distribution of functional...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
American Journal Experts
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10418537/ https://www.ncbi.nlm.nih.gov/pubmed/37577664 http://dx.doi.org/10.21203/rs.3.rs-3219092/v1 |