{
  "id": 542673,
  "title": "Identifying multi-omics biomarkers for ovarian cancer survival estimation",
  "url": "https://urgent.news/2026/08/10/identifying-multi-omics-biomarkers-for-ovarian-cancer-survival",
  "topic": "science",
  "section": "Science",
  "published": "2026-08-10T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.08.04.742866v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Ovarian cancer is among the deadliest gynecologic malignancies, and its molecular heterogeneity limits accurate prognostic stratification. Although multi-omics approaches have improved predictive modeling, many prioritize predictive performance over biological interpretability, limiting their clinical translation. We developed an interpretable three-stage machine learning framework integrating…",
  "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."
}