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	Analisi Metabolomica Basata su Reti	Analisi di Arricchimento dei Percorsi
Campo	Bioinformatica	Bioinformatica
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	2005–2011	2003–2005
Ideatore	Barabasi, Loscalzo and colleagues (network medicine framework); Wishart and Xia (metabolomics network tools)	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Tipo	Systems biology / omics analysis pipeline	Statistical functional annotation method
Fonte seminale	Xia, J., & Wishart, D. S. (2010). MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Research, 38(Web Server issue), W71–W77.	Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.
Alias	metabolic network analysis, systems metabolomics, network metabolomics, metabolite network enrichment	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Correlati	6	6
Sintesi	Network-based metabolomics analysis integrates quantitative metabolite profiling data with biological network structures — metabolic pathways, protein-metabolite interaction graphs, and disease networks — to reveal coordinated biochemical disruptions that individual metabolite lists would miss. Rather than treating each metabolite in isolation, this systems-level approach identifies modules, hubs, and perturbed subnetworks, providing mechanistic insight into how metabolic dysregulation propagates through cellular systems.	Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments.