Methoden vergelijken

	Machine Learning-Assisted Pathway Enrichment Analysis	Gen-setverrijkingsanalyse (GSEA)
Vakgebied	Bio-informatica	Bio-informatica
Familie	Process / pipeline	Process / pipeline
Jaar van ontstaan	2010s–present	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)
Grondlegger	Multiple groups; early integration of ML with PEA circa 2010s (e.g., Ma'ayan Lab, Greene Lab)	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)
Type	Computational pipeline combining statistical enrichment with machine learning	Functional genomics / enrichment analysis
Oorspronkelijke bron	Chen, E. Y., Tan, C. M., Kou, Y., Duan, Q., Wang, Z., Meirelles, G. V., Clark, N. R., & Ma'ayan, A. (2013). Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics, 14, 128.	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.
Aliassen	ML-assisted PEA, ML-based pathway analysis, machine learning pathway enrichment, ML-enhanced gene set enrichment	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment
Verwant	2	5
Samenvatting	Machine learning-assisted pathway enrichment analysis integrates classical statistical pathway enrichment methods — such as over-representation analysis or gene set enrichment analysis — with machine learning algorithms to improve sensitivity, handle high-dimensional omics data, and uncover non-linear biological patterns. The approach moves beyond ranking pathways by p-value alone, using ML models to weight gene contributions, distinguish signal from noise across many samples, and prioritize biologically meaningful pathways in complex datasets.	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.