Multi-omic and computational approaches for biomarker discovery and clinical translation in pediatric sepsis

Sepsis remains one of the most complex and lethal syndromes in pediatric critical care, driven by dysregulated and heterogeneous host responses to infection. Despite decades of biomarker research, few biomarkers have been translated into routine clinical use for diagnosis or prognostication. This is largely because single marker approaches cannot capture the systemic complexity of sepsis pathobiology with high sensitivity and specificity. This review explores how the convergence of multi-omic technologies and computational biology is transforming biomarker discovery, from isolated molecular signals into integrated, systems-level understanding of disease, with an emphasis on pediatric sepsis. Recent omic studies reveal dysregulation across immune, endothelial, metabolic, and microbial networks in sepsis. Advances in bioinformatics and artificial intelligence now enable characterization of complex biological patterns that link molecular profiles into interpretable clinical phenotypes and outcomes. Multi-omic integration represents a paradigm shift in pediatric sepsis research, uniting biomarker discovery with clinical application through biologically coherent, computationally derived signatures. As these approaches mature, they promise to transform pediatric sepsis care from empiric treatment to precision medicine guided by the molecular pathways that define each patient's pathobiology.