{
  "abstract": "Predicting transcriptional responses to genetic perturbations could reduce the experimental burden of functional genomics, but extrapolation to genes that were never perturbed during training remains difficult. We present Stable-Shift, a structured method for estimating unseen-gene responses. Stable-Shift aggregates single-cell measurements into perturbation-level expression shifts, fits a low-rank response basis using training perturbations only, and predicts an unseen gene's coordinates in that basis from biological context. The context combines STRING interactions, network structure, control-cell expression statistics, and Gene Ontology annotations; the evaluated implementation uses graph convolution to integrate these inputs. On the supplied K562 Perturb-seq benchmark, Stable-Shift obtained 0.592 cosine similarity, compared with 0.569 for GEARS, together with higher Spearman correlation and top-gene precision among the evaluated methods. Its mean cosine similarity over five unseen-gene splits was 0.589 +/- 0.008. The same ordering was observed in the supplied graph-aware, residualized, gene-space, and Norman-dataset comparisons. These results support further study of biologically structured latent-response prediction, while the lower gene-space accuracy and sensitivity to sparse graph neighborhoods limit the scope of the present conclusions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Proceedings of the 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (BCB '26), Article 101, 6 pages, 2026",
  "arxiv": "2606.24940",
  "author_total": 2,
  "authors": [
    {
      "name": "Sajib Acharjee Dip"
    },
    {
      "name": "Liqing Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/042mvyt3bxprv.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.24940",
  "fulltext_url": null,
  "id": "042mvyt3bxprv",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.24940",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.GN"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2606.24940",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/042mvyt3bxprv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:30Z",
      "native_id": "2606.24940",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.24940"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.GN",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Stable-Shift: Biologically Structured Prediction of Transcriptional Responses to Unseen Gene Perturbations",
  "title_text": "Stable-Shift: Biologically Structured Prediction of Transcriptional Responses to Unseen Gene Perturbations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:30Z",
  "url": "https://research.draconitic.com/paper/042mvyt3bxprv",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1145/3807503.3820871",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}