手法を比較

	差次的メタボローム解析	機械学習支援メタボロミクス解析
分野	バイオインフォマティクス	バイオインフォマティクス
系統	Process / pipeline	Process / pipeline
提唱年	2000s–2010s (field formalised alongside mass spectrometry advances)	2000s–2010s (rapid adoption 2015–present)
提唱者	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
種類	Quantitative comparative omics pipeline	Integrative analytical pipeline
原典	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.
別名	comparative metabolomics, differential metabolite profiling, metabolomic differential analysis, DMA	ML-metabolomics, chemoinformatics ML, metabolite profiling with machine learning, ML-driven metabolic profiling
関連	6	1
概要	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.