{
  "abstract": "Predicting single-gene knockout effects from wild-type single-cell RNA-seq data remains challenging when perturbation experiments are costly and existing approaches require knockout-specific model training. Here we introduce Topology-Adjusted Knockout (TAKO), a computationally efficient WT-only framework for single-gene KO impact ranking on single-cell gene regulatory networks. The method applies a strict cut-in-and-out intervention on network transition dynamics and quantifies impact by contrasting stationary propagation profiles before and after perturbation under the same restart distribution. In SERGIO simulations, TAKO recovered direct regulator-target structure but did not show a consistent global advantage over a matched absolute Pearson-correlation ranking across network sizes. Direct evaluation against measured responses in Trem2 and Nkx2-1 KO data likewise yielded mixed global discrimination, although TAKO contained more differentially expressed genes among the top-ranked candidates in both examples. These results position TAKO as a fast, interpretable topology-based prioritisation method rather than a generally superior predictor of post-KO expression responses.",
  "abstract_shown": true,
  "affiliations": [
    "Graduate School of Interdisciplinary Information Studies, The University of Tokyo, Tokyo, Japan.",
    "Interfaculty Initiative in Information Studies, The University of Tokyo, Tokyo, Japan."
  ],
  "article_type": "methods article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Hao Wang"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Koji Oba"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/332bxnadt507n.bib",
  "cited_by": 0,
  "doi": "10.3389/fbinf.2026.1924703",
  "fulltext_url": "https://europepmc.org/articles/PMC13714505",
  "id": "332bxnadt507n",
  "issns": [
    "2673-7647"
  ],
  "landing_url": "https://europepmc.org/article/MED/42953009",
  "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": [
    [
      "heading",
      "Methods"
    ],
    [
      "openalex",
      "1312|Gene Regulatory Network Analysis"
    ]
  ],
  "openalex": "W7221830829",
  "pdf_url": "https://europepmc.org/articles/PMC13714505?pdf=render",
  "pmcid": "PMC13714505",
  "pmid": "42953009",
  "published": "2026-10-14",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/332bxnadt507n.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:41Z",
      "native_id": "MED:42953009",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42953009"
    },
    {
      "first_seen": "2026-11-01T12:21:59Z",
      "native_id": "PMC13714505",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fbinf.2026.1924703"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Gene Regulatory Network Analysis",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Topology-adjusted knockout (TAKO): interpretable in silico single-gene knockout ranking on wild-type single-cell gene regulatory networks.",
  "title_text": "Topology-adjusted knockout (TAKO): interpretable in silico single-gene knockout ranking on wild-type single-cell gene regulatory networks.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:21:59Z",
  "url": "https://research.draconitic.com/paper/332bxnadt507n",
  "venue": "Frontiers in bioinformatics",
  "versions": []
}