{
 "id": "acmg_predictor_evaluation_02",
 "category": "ACMG & In Silico Predictors",
 "title": "REVEL Ensemble Pathogenicity Calibration & Machine Learning Performance",
 "description": "Deleterious threshold calibrations, specificity, and ROC performance for REVEL Ensemble.",
 "columns": [
  "Predictor Parameter",
  "Algorithm Architecture",
  "Calibrated Cutoff",
  "Mitotic Specificity",
  "Proband Variant Evaluation",
  "ACMG Evidence Code"
 ],
 "rows": [
  [
   "Algorithm Profile: REVEL Ensemble",
   "Random Forest combining 13 in silico tools",
   "> 0.750 (Damaging)",
   "91.8% in Sarcoma Hotspots",
   "TRIP13 = 0.882 (Pathogenic)",
   "PP3 / PS3 Criteria"
  ],
  [
   "Tested Locus 2A",
   "Gene Model 2",
   "Cutoff Delta 0.08",
   "Deleterious Prediction 2",
   "ClinVar Score 24",
   "PS1 / PM1 Validation"
  ],
  [
   "Tested Locus 2B",
   "Variant Target 2",
   "Percentile 95.30%",
   "Pathogenic Classification 2",
   "Loss of function rank 2",
   "PVS1 Support"
  ],
  [
   "Benchmark ROC 2C",
   "Empirical Calibration Dataset",
   "AUC = 0.906",
   "False positive rate 0.20%",
   "Calibrated on pediatric sarcoma cohort",
   "ACMG Consensus Gate"
  ]
 ],
 "tags": [
  "ACMG",
  "InSilico",
  "REVEL"
 ],
 "row_count": 4,
 "columns_count": 6
}