방법 비교

	베이즈 경로 농축 분석	네트워크 기반 경로 농축 분석
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2001–2007	2002 (seminal network-scoring concept); matured 2010–2015
창시자	Pierre Baldi, Anthony Long; Michael Newton et al. (foundational Bayesian gene-set frameworks)	Ideker, Ozier, Schwikowski, and Siegel (network-based scoring); extended by Vaske et al. (PARADIGM) and others
유형	Probabilistic gene-set testing	Pathway enrichment and network analysis method
원전	Baldi, P., & Long, A. D. (2001). A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics, 17(6), 509–519.	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.
별칭	Bayesian gene-set testing, Bayesian GSEA, Bayesian functional enrichment, BGSEA	network pathway enrichment, network-based enrichment, topology-based pathway analysis, NBPEA
관련	6	1
요약	Bayesian pathway enrichment analysis tests whether a predefined set of genes — a biological pathway — is systematically overrepresented among genes that show evidence of differential activity in an experiment. Unlike classical over-representation tests, it encodes prior biological knowledge as a prior distribution and updates it with the observed expression data, yielding posterior probabilities of enrichment rather than p-values. This probabilistic framing naturally handles small samples, multiple pathways, and uncertainty propagation in a coherent statistical framework.	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.