{
  "abstract": "Cellular reprogramming supports regenerative medicine, yet the endogenous regulators of a specified source-to-target transition remain incompletely defined. Existing computational methods rank candidate regulators from network topology, trajectory dynamics, or cell-state labels, leaving unused the factors experimentally delivered to induce the transition. We present FateDriver, a weakly supervised dual-view graph neural network that jointly represents cells and genes from single-cell transcriptomes, treats the delivered factors as sparse gene-level labels, and ranks the remaining genes for a specified transition under a positive--unlabeled formulation. Across two OSKM datasets and one GMT dataset, FateDriver ranked first or second among six methods in recovering externally curated reference genes, exceeded a supervision-matched non-graph model despite weaker co-expression with the delivered factors, and achieved the highest recovery of protein targets of pluripotency-inducing chemical cocktails in both OSKM datasets. Holding either the source or the endpoint fixed yielded distinct candidate programs, including a DDX58--interferon axis associated with cells lacking endogenous pluripotency markers. Applying the same framework to candidate source--target pairs prioritized distinct candidate origins for two in vivo partially reprogrammed intestinal populations. FateDriver converts sparse experimental knowledge into transition-specific hypotheses for regulator prioritization from single-cell trajectories.",
  "abstract_shown": true,
  "affiliations": [
    "Ewha Womans University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "J. Sung"
    },
    {
      "name": "K. Son"
    },
    {
      "name": "H.-J. Cha"
    },
    {
      "affs": [
        0
      ],
      "name": "W. Kim"
    },
    {
      "name": "H. Lee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8vedvnv1kvx3x.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.27.762150",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.27.762150v1.full",
  "id": "8vedvnv1kvx3x",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.27.762150v1",
  "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": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.27.762150v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8vedvnv1kvx3x.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T00:21:22Z",
      "native_id": "10.64898/2026.10.27.762150",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.27.762150v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "FateDriver: Weakly supervised dual-view graph learning for transition-specific prioritization of candidate regulators in cellular reprogramming",
  "title_text": "FateDriver: Weakly supervised dual-view graph learning for transition-specific prioritization of candidate regulators in cellular reprogramming",
  "type": "preprint",
  "updated_at": "2026-11-03T00:20:21Z",
  "url": "https://research.draconitic.com/paper/8vedvnv1kvx3x",
  "venue": "bioRxiv",
  "versions": []
}