Omics-based computational approaches for biomarker identification, prediction, and treatment of Long COVID

Long COVID, or post-acute sequelae of COVID-19 (PASC), is a major global health problem, with cumulative estimates suggesting that around 400 million people worldwide have been affected. It is characterized by persistent or new symptoms such as fatigue, cognitive impairment, and breathlessness lasting beyond four weeks after acute infection. Diverse clinical manifestations, chronic course, and incompletely understood pathophysiology-including hypotheses involving viral persistence, immune dysregulation, autoimmunity, endothelial dysfunction, and metabolic reprogramming-impede the development of diagnostic criteria, biomarkers, and targeted therapies. We conducted a critical review of 101 Long COVID omics studies, focusing on the computational methods used and their methodological quality. Using standardized criteria, we evaluated study design, statistical rigor, reproducibility, and clinical relevance across genomics, epigenomics, transcriptomics, proteomics, metabolomics, and multiomics integration, and mapped these findings onto regulatory and translational frameworks. Despite substantial methodological heterogeneity, convergent biological signals emerged. Genomic studies implicate risk loci in immune and cardiopulmonary pathways. Epigenomic analyses identify differentially methylated regions in immune and circadian genes. Transcriptomic studies reveal persistent dysregulation of innate immune and coagulation pathways, as well as reproducible molecular endotypes. Proteomic studies consistently show abnormalities in the complement cascade and coagulation, with a small panel of complement proteins showing highly reproducible changes across independent cohorts. Metabolomic studies demonstrate sustained mitochondrial dysfunction and altered cellular bioenergetics for up to two years after infection. Multiomics integration supports at least two major endotypes, characterized by predominant inflammatory versus metabolic dysregulation, and provides a basis for patient stratification and computational treatment discovery. Machine learning models frequently achieve high classification performance, but are rarely externally validated. Critical limitations restrict clinical translation. Most studies are underpowered relative to analytical complexity, use heterogeneous case definitions and controls, and report platform-specific signatures with limited overlap. External validation, preregistered analysis plans, and regulatory-aligned assay development are uncommon. To date, no regulatory-approved diagnostic assay or evidence-based therapeutic intervention has directly emerged from these computational findings. Future progress requires harmonized phenotyping protocols, adequately powered longitudinal cohorts with external validation, integration of spatial omics and explainable artificial intelligence, and early engagement with regulatory and health-technology assessment pathways. This review provides a critical assessment and a translational roadmap, outlining how methodologically robust computational omics can be advanced toward clinically actionable tools for Long COVID.