{
  "id": 7332,
  "url": "https://arxiv.org/abs/2603.11872v2",
  "title": "ELISA: An Interpretable Hybrid Generative AI Agent for Expression-Grounded Discovery in Single-Cell Genomics",
  "summary": "Translating single-cell RNA sequencing (scRNA-seq) data into mechanistic biological hypotheses remains a critical bottleneck, as agentic AI systems lack direct access to transcriptomic representations while expression foundation models remain opaque to natural language. Here we introduce ELISA (Embedding-Linked Interactive Single-cell Agent), an interpretable framework that unifies scGPT expression embeddings with BioBERT-based semantic retrieval and LLM-mediated interpretation for interactive s",
  "authors": "Omar Coser",
  "category": "research",
  "topics": "agents-autonomy,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-12T12:46:22.000Z",
  "fetched_at": "2026-07-14T16:33:08.014Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7332",
  "original_url": "https://arxiv.org/abs/2603.11872v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}