Bandingkan kaedah

	Analisis Pengayaan Laluan Berasaskan Rangkaian	Analisis Pengayaan Set Gen (GSEA)
Bidang	Bioinformatik	Bioinformatik
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	2002 (seminal network-scoring concept); matured 2010–2015	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)
Pengasas	Ideker, Ozier, Schwikowski, and Siegel (network-based scoring); extended by Vaske et al. (PARADIGM) and others	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)
Jenis	Pathway enrichment and network analysis method	Functional genomics / enrichment analysis
Sumber perintis	Ideker, T., Ozier, O., Schwikowski, B., & Siegel, A. F. (2002). Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics, 18(suppl_1), S233–S240.	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	network pathway enrichment, network-based enrichment, topology-based pathway analysis, NBPEA	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment
Berkaitan	1	5
Ringkasan	Network-based pathway enrichment analysis integrates molecular interaction networks — protein-protein interactions, signalling graphs, or gene regulatory networks — with omics measurements to identify biological pathways that are coordinately altered in a condition. Unlike classical over-representation or gene-set enrichment approaches that treat pathway genes as independent lists, this family of methods propagates signals across network edges, capturing the topology of interactions and uncovering dysregulated modules that flat-list enrichment would miss.	Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a predefined set of genes — representing a biological pathway, process, or function — shows statistically significant, coordinated differences between two biological conditions. Unlike simple fold-change filtering, GSEA operates on all measured genes ranked by a correlation metric, detecting subtle but consistent shifts across an entire pathway even when no single gene passes a significance threshold.