Sammenlign metoder

	Nettverksbasert metabolomikkanalyse	Bayesiansk metabolomikk-analyse
Fagfelt	Bioinformatikk	Bioinformatikk
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	2005–2011	2005–2010
Opphavsperson	Barabasi, Loscalzo and colleagues (network medicine framework); Wishart and Xia (metabolomics network tools)	Simon Rogers, Mark Girolami and colleagues (Bayesian NMR metabolomics framework, ~2009); broader Bayesian metabolomics developed through 2000s–2010s
Type	Systems biology / omics analysis pipeline	Probabilistic statistical pipeline
Opprinnelig kilde	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.	Rogers, S., Scheltema, R. A., & Girolami, M. A. (2009). Bayesian analysis of metabolomic NMR data. Bioinformatics, 25(14), 1809-1815.
Alias	metabolic network analysis, systems metabolomics, network metabolomics, metabolite network enrichment	Bayesian metabolomics, probabilistic metabolomics, Bayesian metabolite profiling, Bayesian metabolic flux analysis
Relaterte	6	6
Sammendrag	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.	Bayesian metabolomics analysis applies probabilistic inference to metabolite abundance data — typically from mass spectrometry or NMR spectroscopy — to identify differentially abundant metabolites, annotate spectral features, and integrate pathway knowledge. By encoding prior biological knowledge into prior distributions and propagating uncertainty throughout the analysis, it yields more calibrated probability statements about metabolic differences than classical frequentist testing alone.