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arXiv AI By Zhen Zhou, Jiachen Li, Yuan Liu, Xiaoyong Pan, Hong-Bin Shen

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

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The paper introduces a multimodal framework that learns subcellularly resolved cell embeddings by integrating RNA expression profiles, protein sequence representations, and protein structural information using a cross‑attention architecture. This approach models interactions within distinct subcellular compartments, producing fine‑grained embeddings that capture both molecular expression patterns and functional protein properties. It is presented as the first method to jointly incorporate transcriptomic data, sequence, and structural knowledge for subcellularly resolved cell representation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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