Dec. 3, 2023, noon | Sana Hassan


PepCNN, a deep learning model developed by researchers from Griffith University, RIKEN Center for Integrative Medical Sciences, Rutgers University, and The University of Tokyo, addresses the problem of predicting protein-peptide binding residues. PepCNN outperforms other methods in terms of specificity, precision, and AUC metrics by combining structural and sequence-based information, making it a valuable tool […]

The post Meet PepCNN: A Deep Learning Tool for Predicting Peptide Binding Residues in Proteins Using Sequence, Structural, and Language Model Features appeared first …

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