Salīdzināt metodes

	Mašīnmācīšanās atbalstīta ceļu bagātināšanas analīze	Random Forest
Nozare	Bioinformātika	Mašīnmācīšanās
Saime	Process / pipeline	Machine learning
Izcelsmes gads	2010s–present	2001
Autors	Multiple groups; early integration of ML with PEA circa 2010s (e.g., Ma'ayan Lab, Greene Lab)	Breiman, L.
Tips	Computational pipeline combining statistical enrichment with machine learning	Ensemble (bagging of decision trees)
Pirmavots	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Citi nosaukumi	ML-assisted PEA, ML-based pathway analysis, machine learning pathway enrichment, ML-enhanced gene set enrichment	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Saistītās	2	4
Kopsavilkums	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.