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scAnyOmics: Foundation-Model Distillation for Any-to-Any Completion of Multi-omics Virtual Cells — arXiv:2610.41399

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41399
License (as archived): CC0-1.0
Archived: 2026-11-03T08:01:30.065Z
SHA-256 of the archived text: 58933ca15375b03303cb8e272186a5d73ecba33c5a13816dd3e848e81a19504d
Stable URL of this page: https://metraipolis.com/documento/dc_45ea595babea

How to cite

scAnyOmics: Foundation-Model Distillation for Any-to-Any Completion of Multi-omics Virtual Cells — arXiv:2610.41399 — arXiv (en), https://arxiv.org/abs/2610.41399 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_45ea595babea (Metraipolis archive dc_45ea595babea, sha256 58933ca15375b03303cb8e272186a5d73ecba33c5a13816dd3e848e81a19504d)

BibTeX

@misc{dc45ea595babea,
  title = {scAnyOmics: Foundation-Model Distillation for Any-to-Any Completion of Multi-omics Virtual Cells — arXiv:2610.41399},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41399},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_45ea595babea; sha256 58933ca15375b03303cb8e272186a5d73ecba33c5a13816dd3e848e81a19504d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41399. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recent advances in single-cell technologies have enabled the profiling of multiple molecular modalities, including transcriptomics, proteomics, and chromatin accessibility, providing a more comprehensive understanding of cellular states. However, integrating heterogeneous modalities and predicting missing molecular information remain challenging, particularly when paired measurements are limited. Here, we introduce scAnyOmics, a generative framework that distills knowledge from a frozen single-cell foundation model into a multimodal student model. scAnyOmics integrates scRNA-seq, protein-marker measurements, and scATAC-seq into a shared representation, enabling cross-modal prediction and reconstruction of missing modalities. We assembled 690,880 cells from six publicly available studies, covering both paired and partially paired multi-omics measurements, to systematically evaluate its predictive performance. We further evaluated scAnyOmics on held-out donor data, where it outperformed baseline methods across multiple evaluation metrics. Beyond cross-modal reconstruction, we explored its applications in cell-type annotation, perturbation-response prediction, and biological age estimation. Overall, scAnyOmics provides a unified framework for integrating incomplete single-cell multi-omics data and transferring pretrained biological knowledge, supporting the development of virtual cell models across diverse cellular contexts and experimental platforms.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.41399 — 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 2026-11-03T07:59:38.716Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).