Comparer des méthodes

	Analyse métabolomique basée sur les réseaux	Analyse multi-omique de la métabolomique
Domaine	Bio-informatique	Bio-informatique
Famille	Process / pipeline	Process / pipeline
Année d'origine	2005–2011	2000s–2010s (metabolomics ~2000; multi-omics integration ~2010s)
Auteur d'origine	Barabasi, Loscalzo and colleagues (network medicine framework); Wishart and Xia (metabolomics network tools)	Pioneered collectively; key early integrative frameworks by Nicholson & Lindon (metabolomics) and Hasin, Seldin & Lusis (multi-omics disease mapping)
Type	Systems biology / omics analysis pipeline	Integrative computational pipeline
Source fondatrice	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, I., Verma, S., Kumar, S., Jere, A., & Anamika, K. (2020). Multi-omics data integration, interpretation, and its application. Bioinformatics and Biology Insights, 14, 1177932219899051.
Alias	metabolic network analysis, systems metabolomics, network metabolomics, metabolite network enrichment	metabolomics multi-omics integration, integrated metabolomics, multi-omics metabolite profiling, metabolome-centric multi-omics
Apparentées	6	5
Résumé	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.	Multi-omics metabolomics analysis integrates metabolite profiling data — derived from mass spectrometry or NMR spectroscopy — with genomic, transcriptomic, and/or proteomic datasets to build a system-level view of biological phenotypes. By anchoring integration on the metabolome, which reflects the downstream functional output of gene expression and protein activity, this approach connects upstream molecular variation to observable biochemical states, enabling richer mechanistic insight than any single omics layer alone.