{
  "abstract": "Human genetic variation is a powerful driver of therapeutic target prioritization, yet current models struggle to aggregate variable numbers of variants per gene and are bottlenecked by the scarcity of validated targets. We present Varformer, a multimodal transformer that uses cross-modal attention to integrate contextual representations of missense variants with gene-level features. Trained via positive-unlabeled learning on clinically validated targets across four genetic ancestry groups, Varformer achieves a precision@20 of 0.95, 0.86, and 0.48 across three benchmarks of increasing difficulty. It surpasses a logistic regression ablation by 20% and DrugnomeAI by 23% on the hardest benchmark. Cross-population rank normalization isolates targets with consistent genetic support across ancestry groups. The variant encoder also scores individual variants. Across 1,164 carrier-variant pairs in Genes & Health, Varformer ranked the known pathogenic variant in the carrier's top 20 in 57% of pairs. Varformer is available as an open-source framework for unified target and variant prioritization.",
  "abstract_shown": true,
  "affiliations": [
    "Queen Mary University of London"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "A. Wenteler"
    },
    {
      "name": "V. Neduva"
    },
    {
      "name": "Genes & Health Research Team"
    },
    {
      "name": "C. P. Cabrera"
    },
    {
      "name": "W. Wei"
    },
    {
      "name": "M. R. Barnes"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0bgc4xnrtv5vd.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.16.759653",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.16.759653v1.full",
  "id": "0bgc4xnrtv5vd",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.16.759653v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1311|Genomics and Rare Diseases"
    ]
  ],
  "openalex": "W7223247149",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.16.759653v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0bgc4xnrtv5vd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:12Z",
      "native_id": "10.64898/2026.10.16.759653",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.16.759653v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Population-scale drug target and variant prioritization with Varformer",
  "title_text": "Population-scale drug target and variant prioritization with Varformer",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:12Z",
  "url": "https://research.draconitic.com/paper/0bgc4xnrtv5vd",
  "venue": "bioRxiv",
  "versions": []
}