{
  "id": 10693,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1817043",
  "title": "OMNIS: a spatially informed multi-omics deep-learning framework for tumor recurrence prediction and primary–metastatic tumor differentiation title page",
  "summary": "BackgroundCancer recurrence and distant metastasis are major causes of cancer-related death, yet existing biomarkers and single-omics models have limited accuracy and interpretability across tumor types.MethodsWe developed OMNIS (OMics Network Integration and Spatial representation), a convolutional deep-learning framework that embeds multi-omics profiles into a five-channel genomic image ordered by Hi-C–derived chromosomal proximity. Somatic mutation, copy-number alteration, DNA methylation and",
  "authors": "Junxian Li",
  "category": "research",
  "topics": "safety-alignment,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-15T00:00:00.000Z",
  "fetched_at": "2026-07-16T05:10:56.605Z",
  "source_slug": "x-frontiers-in-artificial-intelligence",
  "source_name": "Frontiers in Artificial Intelligence",
  "source_homepage": "https://www.frontiersin.org/journals/artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/10693",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1817043",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}