Linganisha mbinu

	Uchambuzi wa Metabolomiki kwa Kutumia Mitandao	Uchambuzi wa Proteomiki
Nyanja	Bioinformatiki	Bioinformatiki
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2005–2011	1994–2003 (term coined 1994; shotgun proteomics established early 2000s)
Mwanzilishi	Barabasi, Loscalzo and colleagues (network medicine framework); Wishart and Xia (metabolomics network tools)	Marc Wilkins, Matthias Mann, Ruedi Aebersold (proteome/mass spectrometry foundations)
Aina	Systems biology / omics analysis pipeline	Quantitative omics pipeline
Chanzo asilia	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.	Wilkins, M. R., Sanchez, J.-C., Gooley, A. A., Appel, R. D., Humphery-Smith, I., Hochstrasser, D. F., & Williams, K. L. (1996). Progress with proteome projects: Why all proteins expressed by a genome should be identified and how to do it. Biotechnology and Genetic Engineering Reviews, 13(1), 19–50.
Majina mbadala	metabolic network analysis, systems metabolomics, network metabolomics, metabolite network enrichment	proteomics, mass spectrometry-based proteomics, shotgun proteomics, quantitative proteomics
Zinazohusiana	6	6
Muhtasari	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.	Proteomics analysis is a systematic pipeline for identifying and quantifying proteins in biological samples using mass spectrometry. Starting from raw spectral data, the workflow searches protein sequence databases, estimates abundance across conditions, applies statistical tests for differential expression, and maps findings onto biological pathways. It complements transcriptomics by capturing post-translational regulation and actual protein abundance, and is central to biomarker discovery, drug-target identification, and systems biology.