{
  "id": 18200039,
  "title": "Error-Controlled Discovery of Protein Interactions via Conformal Calibration",
  "url": "https://urgent.news/2026/11/04/error-controlled-discovery-of-protein-interactions-via-conformal",
  "topic": "science",
  "section": "Science",
  "published": "2026-11-04T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.11.03.764142v1?rss=1"
  },
  "original_language": "en",
  "account": "Protein language models (PLMs) excel at predicting protein interactions; however, achieving high benchmark performance does not guarantee accurate discoveries. Researchers have developed PPI-Caliper, a model-agnostic framework that utilizes conformal calibration to identify interactions at a user-defined false discovery rate (FDR) while utilizing held-out labels and without retraining the predictor. The framework was tested across five species, two PLM-based predictors, and two versions of human training data for each predictor. PPI-Caliper maintains the empirical FDR at or below the target level when the calibration process is conducted under in-distribution. When faced with distribution shift, the transferred thresholds tend to become overly conservative if the predictive performance improves; however, the framework may fail to control FDR if the performance deteriorates. PPI-Caliper links pre-trained interaction scores to explicit error targets and highlights the importance of evaluating calibration in addition to predictive accuracy for reliable biological discoveries.",
  "summary": "Protein language models (PLMs) are powerful predictors of protein interactions, but strong benchmark performance does not ensure reliable discoveries. We introduce PPI-Caliper, a model-agnostic framework that adapts conformal calibration to select interactions at user-specified false discovery rate (FDR) levels using held-out labels without retraining the predictor. Across five species, two…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}