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MUSS: A Multi-scale and Sequence-based Model for Single-cell Gene Regulation — arXiv:2611.08086

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.08086
License (as archived): CC0-1.0
Archived: 2026-11-06T14:41:52.335Z
SHA-256 of the archived text: 58c56876acd7895e20f2a7788a6f8aad9a16c53a869c59c8720a50733825fb25
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How to cite

MUSS: A Multi-scale and Sequence-based Model for Single-cell Gene Regulation — arXiv:2611.08086 — arXiv (en), https://arxiv.org/abs/2611.08086 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_9afaedd99c3b (Metraipolis archive dc_9afaedd99c3b, sha256 58c56876acd7895e20f2a7788a6f8aad9a16c53a869c59c8720a50733825fb25)

BibTeX

@misc{dc9afaedd99c3b,
  title = {MUSS: A Multi-scale and Sequence-based Model for Single-cell Gene Regulation — arXiv:2611.08086},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.08086},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_9afaedd99c3b; sha256 58c56876acd7895e20f2a7788a6f8aad9a16c53a869c59c8720a50733825fb25},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.08086. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Genomics (q-bio.GN).
Pagina del lavoro: https://arxiv.org/abs/2611.08086 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-06T14:18:42.651Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).