ArticleNature methods2026
Systematically decoding pathological morphologies and molecular profiles with unified multimodal embedding.
Article in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Integrating pathological morphologies and molecular profiles for clinical biomarker discovery with unified multimodal embedding.Clinical and translational medicine · 2026Article
- Cycle-consistent deep generative modeling unifies cellular states across unpaired spatial and single-cell modalities.bioRxiv : the preprint server for biology · 2026Article
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Authors and funding
5 authors.
Funding
Abstract
Systematic cross-modality inference and integration of pathological morphologies and multilayer molecular profiles have advanced disease biology; however, methodological challenges remain in multimodal learning. Here, we present Multi-Embed, a unified and interpretable framework for multimodal learning between multilevel morphologies and multilayer molecular profiles. Multi-Embed achieves superior performance in morphology-molecule inference and integration, fine-grained tissue architecture identification and spatiotemporal trajectory modeling across diverse benchmark tasks, underscoring its utility for enhancing our understanding of disease pathogenesis.
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42032067What OpenQuestion holds
Registered trials
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