เปรียบเทียบวิธี

	การวิเคราะห์เมแทบอโลมิกส์แบบเครือข่าย	การวิเคราะห์เมแทบอโลมิกส์
สาขาวิชา	ชีวสารสนเทศศาสตร์	ชีวสารสนเทศศาสตร์
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2005–2011	1998–2002
ผู้ริเริ่ม	Barabasi, Loscalzo and colleagues (network medicine framework); Wishart and Xia (metabolomics network tools)	Oliver et al. (coining of 'metabolomics'); Oliver Fiehn (systematic framework)
ประเภท	Systems biology / omics analysis pipeline	Quantitative omics pipeline
แหล่งต้นตำรับ	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.	Fiehn, O. (2002). Metabolomics — the link between genotypes and phenotypes. Plant Molecular Biology, 48(1-2), 155–171.
ชื่อเรียกอื่น	metabolic network analysis, systems metabolomics, network metabolomics, metabolite network enrichment	metabolome profiling, metabolic profiling, metabonomics, metabolite profiling
ที่เกี่ยวข้อง	6	6
สรุป	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.	Metabolomics analysis is the large-scale, systematic measurement of small-molecule metabolites in a biological sample to characterise the metabolome — the complete set of metabolic intermediates and products present under defined conditions. By coupling high-throughput analytical platforms such as mass spectrometry (MS) or nuclear magnetic resonance (NMR) spectroscopy with multivariate statistics and pathway databases, metabolomics bridges the genotype–phenotype gap and captures the downstream functional output of genes, transcripts, and proteins in real time.