AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Pep-PU-GAN is introduced, a deep learning framework that combines positive-unlabeled (PU) learning, generative adversarial networks (GANs), and graph neural networks (GNNs) for peptide classification.
A context-aware multi-task heterogeneous graph neural network (GNN) for predicting ncRNA associations with cancer types and metastatic events and enables context-specific prioritization of ncRNA–cancer–metastasis associations for experimental evaluation.
RulePep combines competitive predictive performance with additive logit reconstruction, rule-level evidence reporting, and predicate-suppression auditing, providing a transparent sequence-based framework for peptide candidate prioritization.
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