1. This article proposes a deep learning architecture for predicting protein-protein interaction interface residue pairs.
2. The architecture is trained on data from multiple databases, improving the accuracy and reliability of the predictions.
3. The approach shows promising results and has potential applications in understanding protein interactions and designing therapeutic interventions.
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