{
  "abstract": "Predicting the effects of individual and combined perturbations is central to identifying context-specific vulnerabilities and designing targeted interventions. Yet learning gene representations that integrate functional relationships with cellular state and transfer across perturbation tasks remains challenging. Here, we present EMBER, a gene-centric foundation model that learns context-dependent functional representations of genes. EMBER combines interaction-guided contrastive learning with masked expression reconstruction during pretraining on a single-cell corpus curated for diversity and balanced sampling across cell types and experimental batches. EMBER decouples gene representation learning from task-specific prediction through a composable embedding framework: the pretrained encoder remains frozen, while lightweight probes combine contextualized gene embeddings to predict individual and combined perturbation effects. The same pretrained representations support single-gene perturbation-response prediction, digenic interaction prediction, and multi-target drug-response prediction. EMBER improves cross-context perturbation-response prediction and genetic-interaction ranking relative to evaluated baselines. EMBER also prioritized SCFD1, a mediator of autophagosome-lysosome fusion, as a candidate dependency during mTOR inhibition. Together, these results establish contextualized gene embeddings as reusable building blocks for modeling genetic and pharmacological perturbations, supporting the prioritization of genetic interactions and context-specific interventions for experimental testing.",
  "abstract_shown": true,
  "affiliations": [
    "Carnegie Mellon University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "N. Ho"
    },
    {
      "name": "E. P. Xing"
    },
    {
      "affs": [
        0
      ],
      "name": "J. Ma"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6ejamz2yryza4.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.28.762655",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762655v1.full",
  "id": "6ejamz2yryza4",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762655v1",
  "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/10.64898/2026.10.28.762655v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6ejamz2yryza4.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T00:21:51Z",
      "native_id": "10.64898/2026.10.28.762655",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762655v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "EMBER: Contextualized gene representations enable compositional prediction across cellular perturbations",
  "title_text": "EMBER: Contextualized gene representations enable compositional prediction across cellular perturbations",
  "type": "preprint",
  "updated_at": "2026-11-03T00:20:25Z",
  "url": "https://research.draconitic.com/paper/6ejamz2yryza4",
  "venue": "bioRxiv",
  "versions": []
}