{
  "id": 18333,
  "url": "https://arxiv.org/abs/2608.08148v1",
  "title": "DoGMA: A Central-Dogma-Guided Foundation Model for Multi-Omics Alignment and Multi-Task Learning in Oncology",
  "summary": "Attention mechanisms have been widely utilized in modern deep learning, and many existing multi-omics models inherit their conventional use to allow unrestricted bidirectional interactions. However, the fundamental logic of life is directional. Existing designs often overlook the directionality suggested by the central dogma, potentially limiting transfer across heterogeneous cancers, downstream tasks, and incomplete modality settings.In this work, we present DoGMA, a central-dogma-guided founda",
  "authors": "Junfei Ling, Bangzheng Pu, Bingsen Xue, Tianle Li, Ruying Hu, Cheng Jin",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-08T14:15:43.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/18333",
  "original_url": "https://arxiv.org/abs/2608.08148v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}