Systematically decoding pathological morphologies and molecular profiles with unified multimodal embedding.

Area of Science:

• Computational biology
• Bioinformatics
• Pathology

Background:

• Integrating diverse biological data, such as pathological morphologies and molecular profiles, is crucial for advancing disease biology.
• Current multimodal learning methods face methodological challenges in effectively combining these complex datasets.
• Understanding disease pathogenesis requires robust frameworks for cross-modality inference and integration.

Purpose of the Study:

• To introduce Multi-Embed, a unified and interpretable framework for multimodal learning.
• To enable effective integration between multilevel morphologies and multilayer molecular profiles.
• To address existing methodological challenges in multimodal learning for biological data.

Main Methods:

• Developed a novel framework named Multi-Embed.
• Implemented multimodal learning techniques to connect morphological and molecular data.
• Validated the framework on diverse benchmark tasks for biological data analysis.

Main Results:

• Multi-Embed demonstrated superior performance in morphology-molecule inference and integration.
• The framework achieved high accuracy in fine-grained tissue architecture identification.
• Successfully modeled spatiotemporal trajectories, enhancing biological insights.

Conclusions:

• Multi-Embed provides a powerful and interpretable approach for multimodal biological data analysis.
• The framework significantly enhances the understanding of disease pathogenesis.
• Highlights the utility of unified frameworks in advancing cross-modality biological research.