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Orthogonal projection reveals that protein language model embeddings capture critical biochemical features, with their removal leading to significant drops in predictive performance.
Despite their architectural differences, Transformer-based genome language models can provide equally reliable biological insights as CNNs, as revealed by attention-based explainability methods.
