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.
2016
15
3298
3307
Saccenti, Edoardo; Menichetti, Giulia; Ghini, Veronica; Remondini, Daniel; Tenori, Leonardo; Luchinat, Claudio
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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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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1062048
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