From Omics to Bedside: The Emerging Role of Self-Supervised Learning in Integrating Multi-Modal Health Data
Author(s):
Qin Long and Omid Panahi
The integration of multi-modal health data spanning genomics, transcriptomics, proteomics, medical imaging, and electronic health records holds transformative potential for precision medicine, yet it faces fundamental barriers: data heterogeneity, high dimensionality, sparse annotations, and privacy constraints. Self supervised learning (SSL) has emerged as a paradigm-shifting approach that leverages vast unlabeled data to learn generalizable representations without costly manual annotations. This review examines how SSL is reshaping multi-modal health data integration across three emerging paradigms: (1) contrastive learning for cross-modal association discovery, as demonstrated by COMICAL which identified genotype-phenotype relationships in neurological disorders using CLIP-style alignment ; (2) masked modeling for unified representation learning across histopathology and molecular profiles, exemplified by MORPHEUS which integrates whole-slide images with transcriptomics, methylomics, and genomics in a shared latent space ; and (3) prototype-driven pretraining for missing-aware multimodal prognosis, as implemented in PRIME which learns robust representations from partially observed clinical cohorts . Across these approaches, SSL consistently outperforms supervised alternatives in data-scarce settings, achieving performance comparable to centralized training while preserving data
locality and institutional privacy. However, significant challenges persist including modality misalignment, the semantic gap between pretraining objectives and clinical utility, limited external validation, and computational costs that hinder broad adoption. Despite these barriers, SSL is accelerating the translation of multi-omics discoveries to clinical practice, moving from isolated biomarker identification to integrated, patient-centered predictive modeling.