{
  "abstract": "Single-cell perturbation profiling measures responses to genetic and chemical interventions, yet most models learn a static map, ignoring how populations move over time and how perturbations combine. AnnFlux, an object-conditioned stochastic differential equation, learns a drift field in latent cell-state space. Conditioning on the perturbing object makes the field queryable one object at a time, yielding per-object drifts comparable across genes and drugs. By learning a drift field tailored to each perturbation context, it interpolates a held-out timepoint in an epithelial-mesenchymal transition time course and predicts unseen perturbations. Beyond point estimates, AnnFlux improves distributional fidelity and predicts responses to held-out perturbation combinations. An IFN-response signature predicted by AnnFlux was associated with TLS proximity in an independent pan-cancer spatial atlas. This framework maps perturbation-driven cell-state evolution as continuous trajectories and represents unseen perturbations using prior-knowledge embeddings.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Integrated Biomedical and Life Science, Korea University, Seoul, 02841, Republic of Korea"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "H. Choi"
    },
    {
      "name": "G. Byeon"
    },
    {
      "name": "H. Park"
    },
    {
      "name": "J. Park"
    },
    {
      "name": "S. Lim"
    },
    {
      "affs": [
        0
      ],
      "name": "J.-Y. An"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4a622328w69tr.bib",
  "cited_by": 2,
  "doi": "10.64898/2026.09.01.748703",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.01.748703v1.full",
  "id": "4a622328w69tr",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.01.748703v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7204154101",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.01.748703v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4a622328w69tr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:42Z",
      "native_id": "10.64898/2026.09.01.748703",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.01.748703v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "AnnFlux: object-conditioned neural stochastic differential equations for single-cell perturbation dynamics",
  "title_text": "AnnFlux: object-conditioned neural stochastic differential equations for single-cell perturbation dynamics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:42Z",
  "url": "https://research.draconitic.com/paper/4a622328w69tr",
  "venue": "bioRxiv",
  "versions": []
}