Computational Methods for the Discovery of Metabolic Markers of Complex Traits
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- @Article{lee:2019:Metabolites,
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author = "Michael Y. Lee and Ting Hu",
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title = "Computational Methods for the Discovery of Metabolic
Markers of Complex Traits",
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journal = "Metabolites",
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year = "2019",
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volume = "9",
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number = "4",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2218-1989",
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URL = "https://www.mdpi.com/2218-1989/9/4/66",
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DOI = "doi:10.3390/metabo9040066",
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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.",
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notes = "also known as \cite{metabo9040066}",
- }
Genetic Programming entries for
Michael Y Lee
Ting Hu
Citations