Salīdzināt metodes

	Diferenciālā metabolomikas analīze	Ar mašīnmācīšanos saistīta metabolomikas analīze
Nozare	Bioinformātika	Bioinformātika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2000s–2010s (field formalised alongside mass spectrometry advances)	2000s–2010s (rapid adoption 2015–present)
Autors	Developed through convergent contributions by multiple groups; XCMS (Siuzdak lab, 2006) and MetaboAnalyst (Wishart lab, 2009–2015) are foundational computational implementations	Convergent development; foundational reviews by Liebal et al. (2020) and earlier multivariate metabolomics work by Trygg, Holmes, and Nicholson
Tips	Quantitative comparative omics pipeline	Integrative analytical pipeline
Pirmavots	Xia, J., Sinelnikov, I. V., Han, B., & Wishart, D. S. (2015). MetaboAnalyst 3.0 — making metabolomics more meaningful. Nucleic Acids Research, 43(W1), W251–W257.	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.
Citi nosaukumi	comparative metabolomics, differential metabolite profiling, metabolomic differential analysis, DMA	ML-metabolomics, chemoinformatics ML, metabolite profiling with machine learning, ML-driven metabolic profiling
Saistītās	6	1
Kopsavilkums	Differential metabolomics analysis is a computational pipeline that identifies metabolites whose abundance levels differ significantly between two or more biological conditions — such as disease versus control, treated versus untreated, or different developmental stages. By integrating mass spectrometry or NMR data with statistical modelling and pathway databases, it translates raw spectral measurements into biologically interpretable lists of perturbed metabolic features and the pathways they implicate.	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.