手法を比較

	ネットワークベースのメタボローム解析	マルチオミクスメタボロミクス解析
分野	バイオインフォマティクス	バイオインフォマティクス
系統	Process / pipeline	Process / pipeline
提唱年	2005–2011	2000s–2010s (metabolomics ~2000; multi-omics integration ~2010s)
提唱者	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)
種類	Systems biology / omics analysis pipeline	Integrative computational pipeline
原典	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.
別名	metabolic network analysis, systems metabolomics, network metabolomics, metabolite network enrichment	metabolomics multi-omics integration, integrated metabolomics, multi-omics metabolite profiling, metabolome-centric multi-omics
関連	6	5
概要	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.