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Subcellularly Resolved Single-Cell Embedding Learning with Transcriptomic data, Protein Structure and Localization Information — arXiv:2609.02344

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.02344
License (as archived): CC0-1.0
Archived: 2026-10-17T03:37:50.907Z
SHA-256 of the archived text: 21a50df22041f4a87433557eb089920cbc0108be907b20562ea3fb29afe5369f
Stable URL of this page: https://metraipolis.com/documento/dc_88d232fe9869

How to cite

Subcellularly Resolved Single-Cell Embedding Learning with Transcriptomic data, Protein Structure and Localization Information — arXiv:2609.02344 — arXiv (en), https://arxiv.org/abs/2609.02344 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_88d232fe9869 (Metraipolis archive dc_88d232fe9869, sha256 21a50df22041f4a87433557eb089920cbc0108be907b20562ea3fb29afe5369f)

BibTeX

@misc{dc88d232fe9869,
  title = {Subcellularly Resolved Single-Cell Embedding Learning with Transcriptomic data, Protein Structure and Localization Information — arXiv:2609.02344},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.02344},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_88d232fe9869; sha256 21a50df22041f4a87433557eb089920cbc0108be907b20562ea3fb29afe5369f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.02344. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Existing cell embedding methods predominantly rely on transcriptomic or proteomic measurements and represent each cell as a holistic entity, thereby overlooking the subcellular localization of individual molecules. Moreover, they rarely incorporate protein structural information, despite its fundamental role in determining molecular interactions and functions. In this work, we propose a multimodal framework for learning subcellularly resolved cell embeddings by jointly leveraging RNA expression profiles, protein sequence representations, and protein structural information. Specifically, we employ a cross-attention architecture to integrate transcriptomic, sequence, and structural modalities and model their interactions within distinct subcellular compartments. The resulting embeddings represent each cell through its fine-grained subcellular organization, capturing both molecular expression patterns and the functional properties of the associated proteins. By learning cell representations at subcellular resolution, our framework preserves spatially organized biological information while integrating complementary signals across multiple molecular levels. To the best of our knowledge, this is the first framework that produces subcellularly resolved cell embeddings by jointly incorporating transcriptomic information, protein sequence representations, and protein structural knowledge within a unified cross-modal learning paradigm.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-03 in cs.AI).
Categorie arXiv: Genomics (q-bio.GN) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.02344 — 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 2026-10-17T03:13:12.539Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-03?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).