Background: Multi-omics data integration is critical for understanding complex biological systems, but traditional approaches face scalability and interpretability challenges.
Methods: We present two complementary frameworks: • MultiOmicsBind – A deep learning framework using ImageBind-inspired binding modality for efficient O(n) complexity multi-modal contrastive learning • OmicsNet – A network-based approach for visualizing and analyzing multi-omics relationships.
Results: MultiOmicsBind successfully integrates static + temporal multi-omics profiles to capture dynamic molecular changes during biological processes to reveal disease progression. OmicsNetmaps multi-omics regulatory networks and uncovers conserved functional modules that coordinate cellular responses across transcriptomic, proteomic, metabolomic and imaging modalities.
Conclusions: These frameworks provide scalable, interpretable solutions for multi-omics integration, enabling both hypothesis-driven and data-driven discovery in systems biology.
