Fine-tuning pre-trained embeddings is necessary for best performance in predicting AAV vector viability, with sequence-level representations excelling post-fine-tuning in datasets with sparse localized mutations.
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Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability
Fine-tuning pre-trained embeddings is necessary for best performance in predicting AAV vector viability, with sequence-level representations excelling post-fine-tuning in datasets with sparse localized mutations.