Entropy-Based Network Representation of the Individual Metabolic Phenotype

Edoardo Saccenti; Giulia Menichetti; Veronica Ghini; Daniel Remondini; Leonardo Tenori; Claudio Luchinat

doi:10.1021/acs.jproteome.6b00454

Entropy-Based Network Representation of the Individual Metabolic Phenotype

Edoardo Saccenti^*, Giulia Menichetti, Veronica Ghini, Daniel Remondini, Leonardo Tenori, Claudio Luchinat

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

19 Citations (Scopus)

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.

Original language	English
Pages (from-to)	3298-3307
Journal	Journal of Proteome Research
Volume	15
Issue number	9
DOIs	https://doi.org/10.1021/acs.jproteome.6b00454
Publication status	Published - 2016

Keywords

metabolite modules
metabolite-metabolite association networks
metabolomics
network multiplex

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1021/acs.jproteome.6b00454

https://edepot.wur.nl/392261Licence: CC BY-NC-ND

INFECT: Improving Outcome of Necrotizing Fasciitis: Elucidation of Complex Host and Pathogen Signatures that Dictate Severity of Tissue Infection
1/01/13 → 30/06/18
Project: EU research project

Cite this

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title = "Entropy-Based Network Representation of the Individual Metabolic Phenotype",

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.",

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author = "Edoardo Saccenti and Giulia Menichetti and Veronica Ghini and Daniel Remondini and Leonardo Tenori and Claudio Luchinat",

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T1 - Entropy-Based Network Representation of the Individual Metabolic Phenotype

AU - Saccenti, Edoardo

AU - Menichetti, Giulia

AU - Ghini, Veronica

AU - Remondini, Daniel

AU - Tenori, Leonardo

AU - Luchinat, Claudio

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - metabolite modules

KW - metabolite-metabolite association networks

KW - metabolomics

KW - network multiplex

U2 - 10.1021/acs.jproteome.6b00454

DO - 10.1021/acs.jproteome.6b00454

M3 - Article

AN - SCOPUS:84985919316

SN - 1535-3893

VL - 15

SP - 3298

EP - 3307

JO - Journal of Proteome Research

JF - Journal of Proteome Research

IS - 9

ER -

Entropy-Based Network Representation of the Individual Metabolic Phenotype

Abstract

Keywords

UN SDGs

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Projects

INFECT: Improving Outcome of Necrotizing Fasciitis: Elucidation of Complex Host and Pathogen Signatures that Dictate Severity of Tissue Infection

Cite this