So sánh phương pháp

	Phân tích làm giàu đường dẫn	Phân tích làm giàu đường dẫn dựa trên mạng lưới
Lĩnh vực	Tin sinh học	Tin sinh học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2003–2005	2002 (seminal network-scoring concept); matured 2010–2015
Người khởi xướng	Mootha et al. (2003); systematised by Subramanian et al. (2005)	Ideker, Ozier, Schwikowski, and Siegel (network-based scoring); extended by Vaske et al. (PARADIGM) and others
Loại	Statistical functional annotation method	Pathway enrichment and network analysis method
Công trình gốc	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.	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.
Tên gọi khác	PEA, overrepresentation analysis, ORA, functional enrichment analysis	network pathway enrichment, network-based enrichment, topology-based pathway analysis, NBPEA
Liên quan	6	1
Tóm tắt	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.	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.