{
  "id": 15838737,
  "title": "Spatially Informed and Direction Sensitive Representation Learning for Spatial Transcriptomics",
  "url": "https://urgent.news/2026/10/20/spatially-informed-and-direction-sensitive-representation-learning",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-20T17:32:57.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.28579v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Cellular states are shaped by both intrinsic gene expression programs and the spatial microenvironment in which cells reside. Spatial transcriptomics makes this context measurable, but effective representation learning requires capturing both spatially informative molecular signals and intercellular organization. Existing approaches address these challenges only partially: conventional gene…",
  "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."
}