Background & Problem Insufficient human-labeled EC number → SBO term pairs limit downstream machine learning (ML) training effectiveness and generalization. Solution Leverage GPT-5 ’s biochemical knowledge to generate 8,000 high-quality EC–SBO pairs as data augmentation corpora to boost downstream model performance. Architecture Model choice: Use GPT-5 (stronger reasoning) instead of GPT-4. Structured output: A unified JSON schema to ensure parsability and consistency. Prompt Engineering Inject biochemical rules (e.g., NAD⁺/NADH redox patterns). Leaf-first strategy in the SBO ontology to avoid over-generalization. Candidate list constraint: force selection from a provided list to reduce hallucinations. Quality Control Require a reason field to enable consistency review. Extract keywords to aid subsequent rule/retrieval checks. Error handling: format validation, retry/backoff, and isolation of problematic samples to keep the p...
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