Multi-view learning and omics integration: a unified perspective with applications to healthcare

01/2026

Multi-view learning and omics integration: a unified perspective with applications to healthcare

Friday 2nd January 2026

Iapaolo V.; Vergani, A.M.; Cavinato, L.; Ieva, F.

Recent technological advances have made it possible to collect diverse biomedical data sources for each individual, ranging from imaging to genetics and digital health records. Integrating such heterogeneous information in a coherent and informative way is a key challenge for modern biomedical data analysis. In this work, we present a unified perspective that bridges the fields of multi-view learning and multiomics integration, which have traditionally developed in parallel but share the same underlying objective. We organize this vast methodological landscape with respect to learning objectives, providing a structured overviewof core paradigms, associated challenges, and emerging directions. Through a case study on UK Biobank data, we highlight the importance of interpretability in biomedical contexts by applying two representative methods, AJIVE and SGCCA, which bridge the multi-omics and multi-viewlearning streams. The results show that integrative approaches provide more informative and clinically meaningful insights than single-view analyses, underscoring their practical relevance for biomedical research.