Global bibliometric mapping of multi-omics approaches in modern medical care: Trends, thematic evolution, and clinical translation
Paper Details
Global bibliometric mapping of multi-omics approaches in modern medical care: Trends, thematic evolution, and clinical translation
Abstract
Multi-omics technologies, which integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics, have rapidly transformed modern medical care. These approaches enable systems-level understanding of disease mechanisms and support precision diagnostics and therapeutics. However, a comprehensive bibliometric overview of the global research landscape in this domain is still lacking. A bibliometric study was conducted on publications retrieved from multiple international scientific databases between 2020 and 2025, yielding 21,600 records. After deduplication and eligibility screening, 2,551 articles were included for performance analysis and science mapping. Descriptive indicators (annual growth, top journals, countries, institutions, and authors) were calculated, while network-based analyses were used to evaluate collaboration patterns, keyword co-occurrence, thematic maps, and thematic evolution. The annual scientific output demonstrated a steady growth, with a pronounced increase after 2022. Precision oncology and biomarker discovery represented the most dominant research cluster, followed by cardiometabolic disease, neuro-omics and aging, infection and immunology, and data integration with artificial intelligence. Keyword network analysis revealed strong interconnections between multi-omics integration, machine learning, single-cell technologies, and liquid biopsy-based clinical applications. Thematic evolution highlighted a shift from foundational omics integration toward multi-modal AI-assisted clinical decision support systems. This bibliometric analysis reveals a rapidly expanding and increasingly interdisciplinary multi-omics research landscape, strongly oriented toward precision and systems medicine. The emergence of single-cell, AI-driven, and liquid-biopsy-based strategies indicates a transition toward real-time, clinically actionable multi-omics frameworks.
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