{
  "abstract": "Single-cell studies are typically initiated from predefined research questions, leaving much of the biological information encoded within existing data unexplored. We formalize open-ended discovery as an analytical paradigm, in which data-derived signals are identified before biological context is interrogated and subsequently evaluated according to their potential to justify prospective experimental investment. Here we develop PROSPECTor, an end-to-end framework that searches for reproducible biological structures across conventional expression representations and diverse foundation-model embeddings, translating robust signals into quantitatively testable candidate hypotheses. Projection into unseen datasets then evaluates their generalizability and phenotype association, providing a scalable screen for candidates that warrant prospective validation. Supported signals emerged from different representation spaces and search strategies. PROSPECTor-nominated hypotheses were then examined in independent biological settings: fibroblast extracellular-matrix programmes demonstrated transferability to an independent mouse cohort with an intervention context, while a patient-resolved gastric-cancer T-cell programme recurred across single-cell, bulk and spatial cohorts. PROSPECTor establishes an auditable framework for systematically revisiting single-cell datasets across expanding representation spaces, turning retrospective collections into prospective resources for biological discovery that can motivate new research questions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.00681",
  "author_total": 4,
  "authors": [
    {
      "name": "Ningxuan Zhang"
    },
    {
      "name": "Ziwei Wang"
    },
    {
      "name": "Ning Xie"
    },
    {
      "name": "Na Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8ffqk2wag2gvf.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.00681",
  "fulltext_url": null,
  "id": "8ffqk2wag2gvf",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.00681",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7206194418",
  "pdf_url": "https://arxiv.org/pdf/2609.00681",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8ffqk2wag2gvf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:05Z",
      "native_id": "2609.00681",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.00681"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Operationalizing open-ended biological discovery across single-cell representations",
  "title_text": "Operationalizing open-ended biological discovery across single-cell representations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:05Z",
  "url": "https://research.draconitic.com/paper/8ffqk2wag2gvf",
  "venue": "arXiv",
  "versions": []
}