{
 "id": "acmg_predictor_evaluation_04",
 "category": "ACMG & In Silico Predictors",
 "title": "SpliceAI CNN Pathogenicity Calibration & Machine Learning Performance",
 "description": "Deleterious threshold calibrations, specificity, and ROC performance for SpliceAI CNN.",
 "columns": [
  "Predictor Parameter",
  "Algorithm Architecture",
  "Calibrated Cutoff",
  "Mitotic Specificity",
  "Proband Variant Evaluation",
  "ACMG Evidence Code"
 ],
 "rows": [
  [
   "Algorithm Profile: SpliceAI CNN",
   "32-Layer Deep Residual Dilated Convolutional Net",
   "> 0.20 (Splice Altering)",
   "95.0% Sensitivity",
   "Identifies Cryptic Splice Hotspots",
   "PP3 / PS3 Criteria"
  ],
  [
   "Tested Locus 4A",
   "Gene Model 4",
   "Cutoff Delta 0.16",
   "Deleterious Prediction 4",
   "ClinVar Score 48",
   "PS1 / PM1 Validation"
  ],
  [
   "Tested Locus 4B",
   "Variant Target 4",
   "Percentile 95.60%",
   "Pathogenic Classification 4",
   "Loss of function rank 4",
   "PVS1 Support"
  ],
  [
   "Benchmark ROC 4C",
   "Empirical Calibration Dataset",
   "AUC = 0.912",
   "False positive rate 0.40%",
   "Calibrated on pediatric sarcoma cohort",
   "ACMG Consensus Gate"
  ]
 ],
 "tags": [
  "ACMG",
  "InSilico",
  "SpliceAI"
 ],
 "row_count": 4,
 "columns_count": 6
}