Interpreting embeddings from genome and protein language models.

Embeddings from genome and protein language models can predict biological features like variant severity, boundaries, homology, and structure, offering high-resolution insights.

• Why it matters: Understanding the biological significance of model embeddings can enhance functional genomics and enzyme engineering, addressing the gap between sequence data and biological interpretation.
• What they did: Researchers reviewed the use of embeddings from DNA and protein language models, highlighting their application in predicting variant effects, boundaries, homology, and structure.
• The result: Findings suggest that embeddings provide a high-resolution, alignment-free framework for genome and proteome analysis, enabling new approaches in functional genomics and enzyme design.