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Metabolites 2019, 9(4), 66; https://doi.org/10.3390/metabo9040066

Computational Methods for the Discovery of Metabolic Markers of Complex Traits

1
Faculty of Medicine, Memorial University, St. John’s, NL A1B 3V6, Canada
2
Department of Computer Science, Memorial University, St. John’s, NL A1B 3X5, Canada
*
Author to whom correspondence should be addressed.
Received: 1 February 2019 / Revised: 19 March 2019 / Accepted: 1 April 2019 / Published: 4 April 2019
(This article belongs to the Special Issue Metabolomics of Complex Traits)
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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. View Full-Text
Keywords: metabolomics; complex diseases; biomarker discovery; machine learning; feature selection; classification; ensemble learning; artificial neural networks; genetic programming metabolomics; complex diseases; biomarker discovery; machine learning; feature selection; classification; ensemble learning; artificial neural networks; genetic programming
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Lee, M.Y.; Hu, T. Computational Methods for the Discovery of Metabolic Markers of Complex Traits. Metabolites 2019, 9, 66.

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