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<p>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.</p>

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Systematically decoding pathological morphologies and molecular profiles with unified multimodal embedding

  • 期刊论文

  • 发表日期：

    2026年4月24日

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  • Peng Zhang,
  • Chaofei Gao,
  • Kui Hua,
  • Zhuoyu Zhang,
  • Shao Li

摘要

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.