Abstract
Metabolomics uses quantitative analyses of metabolites from tissues or bodily fluids to acquire a functional readout of the physiological state. Complex diseases arise from the influence of multiple factors, such as genetics, environment and lifestyle. Since genes, RNAs and proteins converge onto the terminal downstream metabolome, metabolomics datasets offer a rich source of information in a complex and convoluted presentation. Thus, powerful computational methods capable of deciphering the effects of many upstream influences have become increasingly necessary. In this review, the workflow of metabolic marker discovery is outlined from metabolite extraction to model interpretation and validation. Additionally, current metabolomics research in various complex disease areas is examined to identify gaps and trends in the use of several statistical and computational algorithms. Then, we highlight and discuss three advanced machine-learning algorithms, specifically ensemble learning, artificial neural networks, and genetic programming, that are currently less visible, but are budding with high potential for utility in metabolomics research. With an upward trend in the use of highly-accurate, multivariate models in the metabolomics literature, diagnostic biomarker panels of complex diseases are more recently achieving accuracies approaching or exceeding traditional diagnostic procedures. This review aims to provide an overview of computational methods in metabolomics and promote the use of up-to-date machine-learning and computational methods by metabolomics researchers.
Highlights
Over the past decade, the metabolome has been deemed the final frontier for broad, biochemical databases of organismal information among the well-established fields of genomics, transcriptomics, and proteomics [1]
Two machine-learning methods, support vector machine (SVM) and random forest (RF), were used to identify a 26-metabolite panel that performed with 83.33% accuracy, 86.67% sensitivity and 80%
Similar to other complex disease areas, osteoarthritis appears to be trending toward the use of multivariate, heuristic approaches that are apt at generating high-performing predictive models for the endless ways in which the upstream pathways of genes, RNA and proteins may converge
Summary
The metabolome has been deemed the final frontier for broad, biochemical databases of organismal information among the well-established fields of genomics, transcriptomics, and proteomics [1]. Metabolomics is the study of quantifying metabolites and mapping their complex interactions within this domain, which is comprised of the total set of small molecules (
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