เปรียบเทียบวิธี

	การวิเคราะห์เมแทบอโลมิกส์แบบเครือข่าย	Gene Set Enrichment Analysis (GSEA)
สาขาวิชา	ชีวสารสนเทศศาสตร์	ชีวสารสนเทศศาสตร์
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2005–2011	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)
ผู้ริเริ่ม	Barabasi, Loscalzo and colleagues (network medicine framework); Wishart and Xia (metabolomics network tools)	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)
ประเภท	Systems biology / omics analysis pipeline	Functional genomics / enrichment analysis
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.	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.
ชื่อเรียกอื่น	metabolic network analysis, systems metabolomics, network metabolomics, metabolite network enrichment	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment
ที่เกี่ยวข้อง	6	5
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.	Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a predefined set of genes — representing a biological pathway, process, or function — shows statistically significant, coordinated differences between two biological conditions. Unlike simple fold-change filtering, GSEA operates on all measured genes ranked by a correlation metric, detecting subtle but consistent shifts across an entire pathway even when no single gene passes a significance threshold.