{
  "abstract": "Current perturbation response modelling in single-cell transcriptomics assumes conserved cell mass and loses gene expression information to latent-space decoding. We propose DELPHAI, training a fitness network and an optimal transport network jointly without biological priors, and during inference applying a fitness-gated transport with a direct gene-space retrieval. Demonstrated across two benchmark frameworks, DELPHAI ranks first in predicting differentially expressed genes, while revealing which cell lineages a perturbation depletes.",
  "abstract_shown": true,
  "affiliations": [
    "aiPTO TechBio"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "X. Zhang"
    },
    {
      "name": "H. Wu"
    },
    {
      "name": "H. Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ajjgam6gt42cz.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.07.01.735965",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.07.01.735965v2.full",
  "id": "ajjgam6gt42cz",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.07.01.735965v2",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7167499121",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.07.01.735965v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ajjgam6gt42cz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:58Z",
      "native_id": "10.64898/2026.07.01.735965",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.07.01.735965v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "DELPHAI predicts heterogeneous perturbation responses with learned single-cell fitness",
  "title_text": "DELPHAI predicts heterogeneous perturbation responses with learned single-cell fitness",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:58Z",
  "url": "https://research.draconitic.com/paper/ajjgam6gt42cz",
  "venue": "bioRxiv",
  "versions": []
}