We approach here the problem of defining and estimating the nature of the metabolite metabolite association network underlying the human individual metabolic phenotype in healthy subjects. We retrieved significant associations using an entropy-based approach and a multiplex network formalism. We defined a significantly over-represented network formed by biologically interpretable metabolite modules. The entropy of the individual metabolic phenotype is also introduced and discussed.
Entropy-Based Network Representation of the Individual Metabolic Phenotype / Saccenti, Edoardo; Menichetti, Giulia; Ghini, Veronica; Remondini, Daniel; Tenori, Leonardo; Luchinat, Claudio. - In: JOURNAL OF PROTEOME RESEARCH. - ISSN 1535-3893. - STAMPA. - 15:(2016), pp. 3298-3307. [10.1021/acs.jproteome.6b00454]
Entropy-Based Network Representation of the Individual Metabolic Phenotype
SACCENTI, EDOARDO;GHINI, VERONICA;TENORI, LEONARDO;LUCHINAT, CLAUDIO
2016
Abstract
We approach here the problem of defining and estimating the nature of the metabolite metabolite association network underlying the human individual metabolic phenotype in healthy subjects. We retrieved significant associations using an entropy-based approach and a multiplex network formalism. We defined a significantly over-represented network formed by biologically interpretable metabolite modules. The entropy of the individual metabolic phenotype is also introduced and discussed.File | Dimensione | Formato | |
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achemsoENTROPY.pdf
accesso aperto
Tipologia:
Versione finale referata (Postprint, Accepted manuscript)
Licenza:
Open Access
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6.2 MB
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Adobe PDF
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6.2 MB | Adobe PDF | |
achemsoENTROPY.pdf
accesso aperto
Tipologia:
Versione finale referata (Postprint, Accepted manuscript)
Licenza:
Open Access
Dimensione
6.2 MB
Formato
Adobe PDF
|
6.2 MB | Adobe PDF |
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