AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Findings show that performance estimates in protein function prediction should be interpreted as outcomes of complete data-centric workflows rather than isolated properties of predictive models.
A methodological framework for resistance-aware antimicrobial peptide discovery is proposed built upon three complementary principles: explainable and uncertainty-aware prediction, biologically constrained and rule-guided generative design, and iterative design–test–learn workflows capable of continuously incorporating new evidence.
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