{
  "abstract": "Modeling gene regulation in single cells requires jointly representing DNA sequence, chromatin accessibility, and gene expression across regulatory scales. Sequence-based models capture DNA-level regulatory grammar but often lack cell-level resolution, whereas many single-cell omics models represent genes and peaks as fixed-vocabulary tokens without their underlying sequence. Here, we present MUSS, a sequence-based multi-scale model for single-cell gene regulation that preserves both DNA-level and cell-level resolution. MUSS derives peak- and gene-level representations from DNA using a frozen pre-trained sequence encoder and integrates them through peak-peak self-attention and distance-biased peak-gene interactions. Peak masked language modeling followed by training on paired scATAC-seq and scRNA-seq aligns these representations with cell-specific regulatory states. Across gene expression prediction, enhancer-gene linking, motif-guided regulatory prioritization, and zero-shot cell clustering, MUSS achieves strong performance against sequence-based and single-cell omics baselines. It further predicts expression for genes and peaks unseen during task training. These results support sequence-grounded, multi-scale representations for decoding cell-type-specific regulatory programs. Code, datasets, and model checkpoints are available at https://github.com/Lotfollahi-lab/MUSS_neurips26.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: NeurIPS 2026",
  "arxiv": "2611.08086",
  "author_total": 10,
  "authors": [
    {
      "name": "Kangjie Zheng"
    },
    {
      "name": "Amirhossein Vahidi"
    },
    {
      "name": "Liying Jin"
    },
    {
      "name": "Joseph Clarke"
    },
    {
      "name": "Vijaya Baskar MS"
    },
    {
      "name": "Connor Rogerson"
    },
    {
      "name": "Mengji Zhang"
    },
    {
      "name": "Muzlifah Haniffa"
    },
    {
      "name": "Anshul Kundaje"
    },
    {
      "name": "Mo Lotfollahi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ec9cbehehs9rc.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.08086",
  "fulltext_url": null,
  "id": "ec9cbehehs9rc",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.08086",
  "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",
      "q-bio.GN"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.08086",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ec9cbehehs9rc.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T18:21:35Z",
      "native_id": "2611.08086",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.08086"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.GN",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "MUSS: A Multi-scale and Sequence-based Model for Single-cell Gene Regulation",
  "title_text": "MUSS: A Multi-scale and Sequence-based Model for Single-cell Gene Regulation",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:50Z",
  "url": "https://research.draconitic.com/paper/ec9cbehehs9rc",
  "venue": "arXiv",
  "versions": []
}