{
  "abstract": "Motivation\n\nPredicting transcriptional responses to drug perturbations in unseen cellular contexts is a central challenge in single-cell perturbation modeling. This challenge creates a need for models that combine flexible state transitions with structured biological prior knowledge. Existing data-driven models often struggle to generalize across unseen cell types, while many prior-based models rely on static biological graphs without explicitly conditioning prior influence on perturbation-specific target support.Results\n\nWe propose DGP-LA, a dynamic graph-prior latent adaptation framework that refines biological priors and injects them as gated latent corrections within a cell-state-conditioned transition model. DGP-LA couples a transition backbone with perturbation-conditioned sparse graph refinement. It propagates gene-aligned target support over the refined graph and selectively injects the resulting topology-aware signal into the hidden representation. On Tahoe and NeurIPS, DGP-LA improves Pearson Delta relative to State by 8.7% and 35.8% for generalization across unseen cell types. In zero-shot drug perturbation response prediction on unseen cell types, it improves Pearson Delta relative to CRISP by 79.1% and 67.6%. The model also improves DE-gene recovery, response discrimination, and Hallmark pathway recovery, supporting adaptive latent integration of biological priors for more generalizable and biologically interpretable perturbation modeling.Availability\n\nDGP-LA's source code and reproducibility materials are available at https://github.com/bieyl/DGP-LA and archived on Zenodo at https://doi.org/10.5281/zenodo.22155398.",
  "abstract_shown": true,
  "affiliations": [
    "School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China.",
    "Zhongguancun Academy, Beijing 100094, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Yunlong Bie",
      "orcid": "0009-0008-3067-1266"
    },
    {
      "affs": [
        0
      ],
      "name": "Peishan Fan"
    },
    {
      "affs": [
        1
      ],
      "name": "Qijin Yin",
      "orcid": "0000-0001-5284-2259"
    },
    {
      "affs": [
        0
      ],
      "name": "Li Xiao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5yjw8rpc4z1d7.bib",
  "cited_by": 0,
  "doi": "10.1093/bioinformatics/btag747",
  "fulltext_url": "https://europepmc.org/articles/PMC13693364",
  "id": "5yjw8rpc4z1d7",
  "issns": [
    "1367-4803",
    "1367-4811"
  ],
  "landing_url": "https://europepmc.org/article/MED/42865067",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7204669067",
  "pdf_url": "https://europepmc.org/articles/PMC13693364?pdf=render",
  "pmcid": "PMC13693364",
  "pmid": "42865067",
  "published": "2026-10-01",
  "publisher": "Oxford University Press (OUP)",
  "ris": "https://research.draconitic.com/paper/5yjw8rpc4z1d7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:00Z",
      "native_id": "MED:42865067",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42865067"
    },
    {
      "first_seen": "2026-10-30T12:19:38Z",
      "native_id": "10.1093/bioinformatics/btag747",
      "source": "crossref",
      "url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag747/8911852"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Dynamic graph prior-guided latent adaptation for generalizable single-cell perturbation response prediction.",
  "title_text": "Dynamic graph prior-guided latent adaptation for generalizable single-cell perturbation response prediction.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:38Z",
  "url": "https://research.draconitic.com/paper/5yjw8rpc4z1d7",
  "venue": "Bioinformatics (Oxford, England)",
  "versions": []
}