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review-function-predictionlisted

Review computational protein or gene function predictions and fill PredictionReview YAML files using the COR/CNN/LSP/UNC/PLI/NPI/REP biological-validity taxonomy. Use for DeepECTF, BioReason/GO-GPT, InterPro2GO, PANTHER/IBA, CLEAN, GloEC, MAPred, ProteinInfer, or other predicted EC/GO annotations.
ai4curation/ai-gene-review · ★ 23 · AI & Automation · score 74
Install: claude install-skill ai4curation/ai-gene-review
# Review Function Prediction Use this skill to review computational function predictions against allowed evidence and write schema-compatible `PredictionReview` YAML. the predictions are in YAML alongside the main curation review YAMLs: `genes/<TAXON>/<GENE>/<GENE>-<TOOL>-predictions-review.yaml` ```yaml id: <UniProt accession> gene_symbol: <gene symbol> locus_tag: <locus tag if available> taxon: id: NCBITaxon:<taxon id> label: <organism> status: COMPLETE description: >- Brief summary of the prediction review outcome, based only on the allowed evidence for this review mode. source_documents: - <allowed file path used> predictions: - source_method: <do not change> source_version: <do not change> source_reference_id: <do not change if present> predicted_term: id: <do not change> label: <do not change> predicted_term_type: <EC | GO_MF | GO_BP | GO_CC> review: assessment: <COR | CNN | LSP | UNC | PLI | NPI | REP> confidence_score: <0 | 1 | 2> error_type: <omit unless applicable> summary: >- Explain the decision in 3-8 sentences. State what allowed evidence was used, whether the prediction is already present in curated annotations, whether it is more or less specific than existing annotations, and why the chosen category is justified. If using UNC, explicitly say what evidence is missing and avoid speculation. ``` The PMIDs supporting your judgment may come from existin