{
  "id": 5303740,
  "title": "NucleicBERT interprets RNA sequence space through self-supervised language modelling",
  "url": "https://urgent.news/2026/09/03/nucleicbert-interprets-rna-sequence-space-through-self-supervised",
  "topic": "science",
  "section": "Science",
  "published": "2026-09-03T00:00:00.000Z",
  "source": {
    "name": "Nature Machine Intelligence",
    "slug": "nature-machine-intelligence",
    "url": "https://www.nature.com/articles/s42256-026-01295-9"
  },
  "original_language": "en",
  "account": null,
  "summary": "Nature Machine Intelligence, Published online: 03 September 2026; doi:10.1038/s42256-026-01295-9 RNA structure and function are hard to infer because annotations are scarce, despite abundant sequence data. Upadhyay et al. trained a self-supervised model on large-scale RNA data that derives biologically meaningful patterns from sequence correlations.",
  "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."
}