Cross-model validation of the confidence facet bands - #81
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Run measure_confidence_facets.py over three further models beyond yeast-GEM (Human-GEM, iYali, panAsp/pAo) and record the result: - Two of the three bands §6 could not exercise DO fire on other real models -- formula-generic on all three, formula-unparseable on both drafts -- so they are calibrated, not yeast artefacts. Only charge-unknown stays fixture-only across all four (a mass-balanced reaction with a charge-less metabolite is genuinely rare). - gpr+literature fires only where reactions carry a pubmed annotation. - The gene-rubric-vs-recorded-Confidence-Level check needs a per-reaction-curated model: iYali and pAo record a blanket value, so they are not graded ground truth the way yeast-GEM is. Also generalise the script to load SBML or cobra YAML/JSON by extension (Human-GEM ships as YAML). Docs: §6 caveat resolved with a pointer to §10.2; §10.2 written up and marked done. -W docs build clean.
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Runs the
equationandgene_associationscorers over three more models — Human-GEM (12 877 rxns),iYali (1924) and panAsp/pAo (1917) — and generalises
scripts/measure_confidence_facets.pyto loadSBML or cobra YAML/JSON by extension.
Equation-band counts vs yeast-GEM:
formula-generic(0.3)formula-unparseable(0.3)charge-unknown(0.6)Two of the three bands yeast-GEM never fires on hit other real models, so they are calibrated, not yeast
artefacts. Only
charge-unknownstays fixture-only — a mass-balanced reaction with a charge-less metaboliteis rare (curated models charge everything; drafts fail mass balance first).
gpr+literature(0.9) fires only where reactions carry apubmedannotation (iYali 749; the others 0). Thegene-rubric-vs-
Confidence Levelcheck needs a per-reaction-curated model: iYali and pAo record a blanketvalue (all 2, all 0), so only yeast-GEM's varied notes are usable ground truth.