{
  "id": 16122,
  "url": "https://arxiv.org/abs/2608.01669v1",
  "title": "CellPrism: A Visual Analytics System for Exploring AI-Driven Virtual Cells in Drug Discovery",
  "summary": "Gene perturbation analysis plays a critical role in drug discovery by enabling researchers to investigate how interventions on specific genes influence global gene expression patterns within cells. Recent advances in artificial intelligence-driven virtual cell models have made it possible to predict gene expression outcomes for a wide range of perturbation strategies in silico, substantially reducing reliance on costly and time-consuming biological experiments. However, effectively exploring and",
  "authors": "Chuhan Shi, Zijian Guo, Zelin Zang, Chengbo Zheng, Ding Ding, Rui Sheng",
  "category": "research",
  "topics": "healthcare,finance-investment,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-03T04:04:04.000Z",
  "fetched_at": "2026-08-04T05:10:21.797Z",
  "source_slug": "x-arxiv-cs-hc",
  "source_name": "arXiv cs.HC",
  "source_homepage": "https://arxiv.org/list/cs.HC/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16122",
  "original_url": "https://arxiv.org/abs/2608.01669v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}