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Achieving a 0.707 intersection-over-union in sand-boil segmentation, this framework effectively eliminates common biases in model training and evaluation.
By fusing protein language model embeddings with structural data, ESMDisPred achieves state-of-the-art accuracy in predicting intrinsically disordered proteins, a feat that could accelerate drug discovery and structural biology.