{
  "id": 9800045,
  "title": "Predictive Modeling of Cancer Cell Growth Kinetics with Machine Learning",
  "url": "https://urgent.news/2026/09/25/predictive-modeling-of-cancer-cell-growth-kinetics-with-machine",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-25T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.19.752932v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Quantitative characterization of cell proliferation is central to preclinical drug discovery. Here, we evaluated Random Forest (RF) regression for predicting confluence-based cell growth trends using data from human cancer cell lines and benchmarked its performance against the widely used logistic and Gompertz models. The RF model achieved higher predictive accuracy within the evaluated dataset,…",
  "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."
}