Módszerek összehasonlítása

	Hálózatalapú génkészlet-gazdagítás-elemzés	Hálózat-alapú GWAS – Hálózat-alapú Genom-szintű Asszociációs Vizsgálat
Tudományterület	Bioinformatika	Bioinformatika
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	2010 (NetGSA); field consolidated 2010-2015	2011–2013 (early tools); mature framework by 2015
Megalkotó	Ali Shojaie & George Michailidis (NetGSA); broader network-propagation approaches by multiple groups (~2010-2015)	Jia et al. (dmGWAS, 2011); Baranzini et al.; multiple concurrent groups
Típus	Network-informed statistical enrichment test	Network-augmented association analysis
Alapmű	Shojaie, A., & Michailidis, G. (2010). Network enrichment analysis in complex experiments. Statistical Applications in Genetics and Molecular Biology, 9(1), Article 22.	Wang, Q., Yu, H., Zhao, Z., & Jia, P. (2015). EW_dmGWAS: edge-weighted dense module search for genome-wide association studies and gene expression profiles. Bioinformatics, 31(15), 2591–2594.
Alternatív nevek	network GSEA, network-propagation GSEA, NetGSA, graph-informed gene set testing	network GWAS, gene network GWAS, network-informed GWAS, NbGWAS
Kapcsolódó	5	6
Összefoglaló	Network-based gene set enrichment analysis (network GSEA) extends classical GSEA by incorporating biological interaction networks — such as protein-protein interaction (PPI) or co-expression graphs — into the enrichment test. Instead of treating each gene independently, the method propagates differential expression signals across network edges, allowing genes that are co-regulated or functionally connected to jointly support the significance of a gene set. The result is a biologically coherent enrichment score that accounts for pathway topology and gene-gene dependencies.	Network-based GWAS integrates conventional genome-wide association study results with biological network data — such as protein-protein interaction (PPI) networks or gene co-expression graphs — to identify disease-relevant gene modules or subnetworks. Instead of reporting only the top individual SNPs, this approach propagates association signals through molecular interaction networks, surfacing gene clusters whose collective signal implicates them in complex-trait biology even when no single variant reaches genome-wide significance alone.