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Protein matchmaking through representation learning
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DOI：10.1016/j.cels.2021.09.007.png)
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Abstract
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Sledzieski, Singh, Cowen, and Berger employ representation learning to predict protein interactions and associations, additionally identifying binding residues between protein pairs. Generalizability is showcased by training on one organism while evaluating on others. The work exemplifies how transfer of AI-learned representations can advance knowledge in molecular biology.
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