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	Analisi proteomica multi-omica	Analisi di Arricchimento dei Percorsi
Campo	Bioinformatica	Bioinformatica
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	2010s (integrative multi-omics frameworks emerged ~2012–2019)	2003–2005
Ideatore	Le Cao, K.-A. and colleagues (mixOmics/DIABLO framework); broader field rooted in Aebersold & Mann proteomics work	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Tipo	Integrative computational pipeline	Statistical functional annotation method
Fonte seminale	Rohart, F., Gautier, B., Singh, A., & Le Cao, K.-A. (2017). mixOmics: An R package for omics feature selection and multiple data integration. PLOS Computational Biology, 13(11), e1005752.	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.
Alias	integrative proteomics, multi-omics proteomics integration, proteogenomics multi-omics, cross-omics proteomics	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Correlati	6	6
Sintesi	Multi-omics proteomics analysis integrates protein abundance data from mass spectrometry with at least one additional omics layer — such as genomics, transcriptomics, or metabolomics — to build a systems-level view of biological regulation. Rather than analyzing proteins in isolation, this approach correlates proteomic profiles with upstream molecular events (e.g., DNA variants, mRNA levels) and downstream functional readouts (e.g., metabolite concentrations), enabling discovery of regulatory drivers that single-omics analyses would miss.	Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments.