{
  "id": 17950009,
  "title": "When perfect is a warning: explanations expose shortcuts that accuracy hides in cancer gene-expression classifiers",
  "url": "https://urgent.news/2026/11/03/when-perfect-is-a-warning-explanations-expose-shortcuts-that-accuracy",
  "topic": "health",
  "section": "Health & Medicine",
  "published": "2026-11-03T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.11.02.763649v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "When the classes in a training set come from different data sources, a classifier can score near-perfectly while learning where a sample came from. We show this for cancer classifiers trained on tumours from The Cancer Genome Atlas and normal tissue from the Genotype Tissue Expression project. Six model families, including a tabular foundation model, reached an area under the curve of 1.000 yet…",
  "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."
}