ProteinBERT: a universal deep-learning model of protein sequence and function

SUMMARY: Self-supervised deep language modeling has shown unprecedented success across natural language tasks, and has recently been repurposed to biological sequences. AVAILABILITY AND IMPLEMENTATION: Code and pretrained model weights are available at https://github.com/nadavbra/protein_bert.

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• 1 However, existing models and pretraining methods are designed and optimized for text analysis.
• 2 We introduce ProteinBERT, a deep language model specifically designed for proteins.
• 3 Our pretraining scheme combines language modeling with a novel task of Gene Ontology (GO) annotation prediction.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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