Salīdzināt metodes

	Epigenomu plaša asociācijas pētījumu (ML-EWAS) ar mašīnmācīšanos atbalstu	Random Forest
Nozare	Bioinformātika	Mašīnmācīšanās
Saime	Process / pipeline	Machine learning
Izcelsmes gads	2010s (methodological consolidation ~2015–2020)	2001
Autors	Teschendorff, Relton, and others in the epigenomics field	Breiman, L.
Tips	Integrative omics analysis pipeline	Ensemble (bagging of decision trees)
Pirmavots	Teschendorff, A. E., & Relton, C. L. (2018). Statistical and integrative system-level analysis of DNA methylation data. Nature Reviews Genetics, 19(3), 129–147.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Citi nosaukumi	ML-EWAS, machine learning EWAS, ML-assisted EWAS, epigenome-wide association study with machine learning	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Saistītās	3	4
Kopsavilkums	Machine learning-assisted EWAS integrates conventional epigenome-wide association testing with machine learning models to identify DNA methylation sites associated with a phenotype of interest. By combining the statistical rigour of EWAS with the pattern-recognition power of algorithms such as elastic net, random forest, or gradient boosting, this approach handles the extreme dimensionality of methylation arrays (450,000–850,000 CpG sites) more effectively than univariate testing alone, and can capture non-linear and interaction effects that standard linear models miss.	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.