Multi-Omics as the Default: Integrative Models Across Molecular Layers

The study of complex biological systems increasingly requires approaches that move beyond isolated, single-omic analyses. In many areas of life science and biomedicine, “integrative” research is no longer understood as the parallel use of genomics, transcriptomics, epigenomics, proteomics, or metabolomics in separate analytical streams. Instead, the field is shifting toward joint, multi-layer models that combine these data types to capture biological processes more holistically and generate more robust mechanistic insight.This Research Topic will explore the growing transition toward multi-omics as a default framework for systems-level discovery. By integrating information across molecular layers, researchers can better characterize regulatory networks, identify biomarkers with greater precision, and improve the interpretation of dynamic biological states in health, disease, and environmental contexts. These approaches are increasingly important for understanding cellular heterogeneity, disease progression, host–microbe interactions, and treatment response, among many other applications.We welcome contributions that present methodological advances, computational frameworks, biological discoveries, and translational applications enabled by integrated multi-omics approaches. Topics of interest include, but are not limited to, the development of joint models across omic layers, data fusion and harmonization strategies, interpretable machine learning for multi-omics, and studies demonstrating how integrative analyses reveal insights that would not be accessible through single-omic approaches alone.By bringing together contributions from across disciplines, this Research Topic aims to highlight how multi-omics integration is reshaping systems biology and becoming a foundational strategy for biological and biomedical research.Relevant subtopics may include• Joint modeling of genomic, transcriptomic, epigenomic, proteomic, and metabolomic data• Multi-omics integration for biomarker discovery and disease stratification• Data harmonization, normalization, and fusion across molecular layers• Machine learning and AI methods for integrative multi-omics analysis• Network-based and systems-level interpretation of multi-omics datasets• Single-cell and spatial multi-omics approaches• Multi-omics applications in precision medicine and therapy response• Integrative omics in microbiology, immunology, cancer, and complex diseases• Challenges in reproducibility, interpretability, and scalability of multi-omics models Topic editors: Shaoyu Li, Muhammad Alaa Eldeen, Salima Akter Source: https://www.frontiersin.org/research-topics/79966/multi-omics-as-the-default-integrative-models-across-molecular-layers