Representation learning for low-data protein fitness prediction using sequence–structure embeddings and graph neural networks
Accurate prediction of mutational fitness effects remains challenging in low-data protein engineering settings, where experimentally characterized variants are limited. Although recent protein language models and structure-aware neural networks have demonstrated strong performance on large-scale datasets, the relative...
Chanoknun Phunnasorn, Narupat Hongdilokkul, Wasakorn Laesanklang et al.
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