Σύγκριση μεθόδων

	Ανάλυση Μεταβολομικής με Υποβοήθηση Μηχανικής Μάθησης	Τυχαίο Δάσος
Πεδίο	Βιοπληροφορική	Μηχανική Μάθηση
Οικογένεια	Process / pipeline	Machine learning
Έτος προέλευσης	2000s–2010s (rapid adoption 2015–present)	2001
Δημιουργός	Convergent development; foundational reviews by Liebal et al. (2020) and earlier multivariate metabolomics work by Trygg, Holmes, and Nicholson	Breiman, L.
Τύπος	Integrative analytical pipeline	Ensemble (bagging of decision trees)
Θεμελιώδης πηγή	Liebal, U. W., Phan, A. N. T., Sudhakar, M., Raman, K., & Blank, L. M. (2020). Machine learning applications for mass spectrometry-based metabolomics. Metabolites, 10(6), 243.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Εναλλακτικές ονομασίες	ML-metabolomics, chemoinformatics ML, metabolite profiling with machine learning, ML-driven metabolic profiling	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Συναφείς	1	4
Σύνοψη	Machine learning-assisted metabolomics analysis is an integrative bioinformatics pipeline that couples untargeted or targeted metabolite profiling — via mass spectrometry or NMR — with supervised and unsupervised ML algorithms to discover biomarkers, classify phenotypes, and model metabolic states. By handling the extreme dimensionality and collinearity inherent in metabolomics datasets (hundreds to thousands of features, tens to hundreds of samples), ML methods such as random forests, support vector machines, and neural networks extract biologically interpretable patterns that classical univariate statistics routinely 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.