Sammenlign metoder

	Bayesiansk proteomikk-analyse	Pathway Enrichment Analysis
Fagfelt	Bioinformatikk	Bioinformatikk
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	2000s (major developments 2003–2010)	2003–2005
Opphavsperson	Multiple contributors; foundational statistical frameworks by Nesvizhskii, Kall, Choi, and colleagues	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Type	Probabilistic inference pipeline	Statistical functional annotation method
Opprinnelig kilde	Kall, L., Canterbury, J. D., Weston, J., Noble, W. S., & MacCoss, M. J. (2008). Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nature Methods, 5(11), 923–925.	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	Bayesian protein quantification, Bayesian peptide inference, probabilistic proteomics, Bayesian mass spectrometry analysis	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Relaterte	6	6
Sammendrag	Bayesian proteomics analysis applies probabilistic models to mass spectrometry data to identify peptides, infer protein presence, and quantify differential protein abundance across conditions. By encoding prior knowledge and propagating uncertainty through each step of the pipeline, Bayesian approaches produce calibrated posterior probabilities of identification and quantification rather than simple point estimates, enabling more principled control of false discovery rates and more honest reporting of uncertainty than purely frequentist alternatives.	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.