Vibrational mode graphs from molecular dynamics enable sequence-free protein function prediction via graph neural networks, with entrainment improving signals for collective dynamics.
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CuMMI applies curriculum learning across progressively complex biofluids to a multimodal model integrating protein sequence, structure, and 37 experimental features, achieving mean classification metrics above 0.75 on temporal, nanomaterial-held-out, and protein-held-out tests.
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Frequency-Space Mechanics: A Sequence and Coordinate-Free Representation for Protein Function Prediction
Vibrational mode graphs from molecular dynamics enable sequence-free protein function prediction via graph neural networks, with entrainment improving signals for collective dynamics.
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Curriculum-guided multimodal representation learning enables generalizable prediction of nanomaterial-protein interactions
CuMMI applies curriculum learning across progressively complex biofluids to a multimodal model integrating protein sequence, structure, and 37 experimental features, achieving mean classification metrics above 0.75 on temporal, nanomaterial-held-out, and protein-held-out tests.