{
  "id": 17957626,
  "title": "RELAY: Relational In-Context Learning for Cellular Perturbation Prediction",
  "url": "https://urgent.news/2026/11/03/relay-relational-in-context-learning-for-cellular-perturbation",
  "topic": "science",
  "section": "Science",
  "published": "2026-11-03T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.10.31.763027v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Cellular responses to perturbations vary across multiple experimental variables, such as perturbation identity, cell type, donor, and dose, defining a multidimensional condition space in which each measured population occupies one coordinate. Existing methods predict unmeasured conditions using explicit representations of these variable values, generalizing unseen values therefore depends on the…",
  "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."
}