Unveiling Interpretable Features in Protein Language Models through Sparse Autoencoders

Protein language models (PLMs) have significantly advanced protein structure and function prediction by leveraging the vast diversity of naturally evolved protein sequences. However, their internal mechanisms still need to be better understood. Recent interpretability research offers tools to analyze the representations these models learn, which is essential for improving model design and uncovering biological insights. […]

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