Compară metode

	Epigenetică Asociativă la Nivel de Genom (Network EWAS) bazată pe rețele	Analiza de îmbogățire a căilor metabolice
Domeniu	Bioinformatică	Bioinformatică
Familie	Process / pipeline	Process / pipeline
Anul apariției	2010s, consolidating 2012–2018	2003–2005
Autorul original	Adapted from EWAS (Rakyan et al., 2011) and network-based genomic methods (e.g., Ideker & Sharan, 2008)	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Tip	Integrative epigenomic analysis	Statistical functional annotation method
Sursa seminală	Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541.	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.
Denumiri alternative	network EWAS, network-integrated EWAS, graph-based EWAS, network-based DNA methylation analysis	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Înrudite	6	6
Rezumat	Network-based EWAS extends conventional epigenome-wide association studies by overlaying differentially methylated positions or regions onto biological interaction networks — such as protein-protein interaction, co-expression, or gene regulatory networks — to identify functionally coherent epigenetic modules rather than isolated CpG hits. This integration increases statistical power for detecting weak signals and reveals coordinated epigenetic dysregulation across pathways.	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.