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

Authors: Kangjie Zheng , Amirhossein Vahidi , Liying Jin , Joseph Clarke , Vijaya Baskar MS , Connor Rogerson , Mengji Zhang , Muzlifah Haniffa , Anshul Kundaje , Mo Lotfollahi

View a PDF of the paper titled MUSS: A Multi-scale and Sequence-based Model for Single-cell Gene Regulation, by Kangjie Zheng and 9 other authors

View PDF

HTML (experimental)

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 this https URL .

Comments:

30 pages, 5 figures; includes references, appendices, and the NeurIPS checklist

Subjects:

Artificial Intelligence (cs.AI) ; Genomics (q-bio.GN)

Cite as:

arXiv:2611.08086 [cs.AI]

(or

arXiv:2611.08086v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.08086

Focus to learn more

arXiv-issued DOI via DataCite (pending registration)

Journal reference:

NeurIPS 2026

Submission history

From: Kangjie Zheng [ view email ]

[v1]

Thu, 5 Nov 2026 14:34:07 UTC (303 KB)

Full-text links:

Access Paper:

View a PDF of the paper titled MUSS: A Multi-scale and Sequence-based Model for Single-cell Gene Regulation, by Kangjie Zheng and 9 other authors

View PDF

HTML (experimental)

TeX Source

view license

Additional Features

Audio Summary

Current browse context:

cs.AI

< prev

next >

new

recent

| 2026-11

Change to browse by:

cs

q-bio

q-bio.GN

References & Citations

SciX

Google Scholar

Semantic Scholar

export BibTeX citation

Loading...

BibTeX formatted citation

loading...

Data provided by:

Bookmark

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer ( What is the Explorer? )

Connected Papers Toggle

Connected Papers ( What is Connected Papers? )

Litmaps Toggle

Litmaps ( What is Litmaps? )

scite.ai Toggle

scite Smart Citations ( What are Smart Citations? )

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv ( What is alphaXiv? )

Links to Code Toggle

CatalyzeX Code Finder for Papers ( What is CatalyzeX? )

DagsHub Toggle

DagsHub ( What is DagsHub? )

GotitPub Toggle

Gotit.pub ( What is GotitPub? )

Huggingface Toggle

Hugging Face ( What is Huggingface? )

ScienceCast Toggle

ScienceCast ( What is ScienceCast? )

Demos

Demos

Replicate Toggle

Replicate ( What is Replicate? )

Spaces Toggle

Hugging Face Spaces ( What is Spaces? )

Spaces Toggle

TXYZ.AI ( What is TXYZ.AI? )

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower ( What are Influence Flowers? )

Core recommender toggle

CORE Recommender ( What is CORE? )

Author

Venue

Institution

Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Which authors of this paper are endorsers? |

Disable MathJax ( What is MathJax? )