Toward Trustworthy Multi-View Representation With Fine-Grained Explainability Embeddings

Area of Science:

• Biomedical Data Science
• Computational Biology
• Artificial Intelligence in Medicine

Background:

• Multiomics co-learning offers significant benefits in biomedical research but faces challenges with data diversity and complex relationships.
• Naive multi-view learning methods often yield spurious correlations and biased signatures, hindering clinical translation, especially with limited data.
• Existing methods struggle to extract reliable cross-omics associations for accurate disease prediction.

Purpose of the Study:

• To introduce a novel scheme, Causality-driven Trustworthy Multi-View maPping (Cad-TMVP), for robust multiomics data analysis.
• To overcome limitations of existing methods in handling data diversity, spurious correlations, and scarce clinical data.
• To develop a trustworthy multi-modal learning framework for improved disease prediction and interpretation.

Main Methods:

• Designed a fined multi-directional mapping module for extracting co-expression patterns and interpretability factors across modalities.
• Implemented dynamic mechanisms for adaptive loss-term reweighting and trustworthy multi-modal integration.
• Developed a cooperative learning module for simultaneous automated diagnosis and result interpretation, alongside an efficient search strategy.

Main Results:

• Cad-TMVP established new state-of-the-art results across various multiomics data settings.
• The approach demonstrated excellent interpretability, enhancing the clinical relevance of learned representations.
• Experiments confirmed the method's flexibility and versatility in real-world biomedical applications.

Conclusions:

• Cad-TMVP offers a powerful and trustworthy solution for multiomics co-learning, mitigating spurious correlations and biased signatures.
• The method enhances downstream tasks like automated diagnosis and interpretation, facilitating clinical knowledge translation.
• Cad-TMVP sets a new paradigm for trustworthy multi-modal learning in biomedical research.