Linganisha mbinu

	Uchanganuzi wa Kukuza Kundi la Jeni la Multi-omics	Uchambuzi wa Proteomiki
Nyanja	Bioinformatiki	Bioinformatiki
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2005 (GSEA foundation); multi-omics extensions ~2013–2020	1994–2003 (term coined 1994; shotgun proteomics established early 2000s)
Mwanzilishi	Extended from Subramanian et al. (2005); multi-omics integration formalized ~2010s	Marc Wilkins, Matthias Mann, Ruedi Aebersold (proteome/mass spectrometry foundations)
Aina	Integrative enrichment analysis pipeline	Quantitative omics pipeline
Chanzo asilia	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.	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	multi-omics GSEA, integrated GSEA, cross-omics pathway enrichment, multi-layer GSEA	proteomics, mass spectrometry-based proteomics, shotgun proteomics, quantitative proteomics
Zinazohusiana	6	6
Muhtasari	Multi-omics gene set enrichment analysis (multi-omics GSEA) is a computational pipeline that applies GSEA logic simultaneously across two or more molecular measurement layers — such as transcriptomics, proteomics, and metabolomics — to identify biological pathways or gene sets that are coordinately dysregulated across omics platforms. By integrating ranked molecular signatures from each layer, it reveals pathway-level convergence that no single omics platform could detect alone.	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.