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Operationalizing open-ended biological discovery across single-cell representations — arXiv:2609.00681

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.00681
License (as archived): CC0-1.0
Archived: 2026-10-17T07:32:00.971Z
SHA-256 of the archived text: 4f579981c21e4290141988b73292fae707a5cacd1815072037aebfadfae81638
Stable URL of this page: https://metraipolis.com/documento/dc_b3c19d7c219f

How to cite

Operationalizing open-ended biological discovery across single-cell representations — arXiv:2609.00681 — arXiv (en), https://arxiv.org/abs/2609.00681 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_b3c19d7c219f (Metraipolis archive dc_b3c19d7c219f, sha256 4f579981c21e4290141988b73292fae707a5cacd1815072037aebfadfae81638)

BibTeX

@misc{dcb3c19d7c219f,
  title = {Operationalizing open-ended biological discovery across single-cell representations — arXiv:2609.00681},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.00681},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_b3c19d7c219f; sha256 4f579981c21e4290141988b73292fae707a5cacd1815072037aebfadfae81638},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.00681. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: scienze della vita e salute (retro-catalogo arXiv: annunci del 2026-09-02 in q-bio.QM).
Categorie arXiv: Quantitative Methods (q-bio.QM).
Pagina del lavoro: https://arxiv.org/abs/2609.00681 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T07:21:08.402Z dalla pagina https://arxiv.org/catchup/q-bio.QM/2026-09-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).