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Scale-free networks in metabolomics
Metabolomics is an expanding discipline in biology. It is the process of portraying the phenotype of a cell, tissue or species organism using a comprehensive set of metabolites. Therefore, it is of interest to understand complex systems such as metabolomics using a scale-free topology. Genetic netwo...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Biomedical Informatics
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5953857/ https://www.ncbi.nlm.nih.gov/pubmed/29785073 http://dx.doi.org/10.6026/97320630014140 |
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author | Rajula, Hema Sekhar Reddy Mauri, Matteo Fanos, Vassilios |
author_facet | Rajula, Hema Sekhar Reddy Mauri, Matteo Fanos, Vassilios |
author_sort | Rajula, Hema Sekhar Reddy |
collection | PubMed |
description | Metabolomics is an expanding discipline in biology. It is the process of portraying the phenotype of a cell, tissue or species organism using a comprehensive set of metabolites. Therefore, it is of interest to understand complex systems such as metabolomics using a scale-free topology. Genetic networks and the World Wide Web (WWW) are described as networks with complex topology. Several large networks have vertex connectivity that goes beyond a scale-free power-law distribution. It is observed that (a) networks expand constantly by the addition of recent vertices, and (b) recent vertices attach preferentially to sites that are already well connected. Scalefree networks are determined with precision using vital features such as a structure, a disease and a patient. This is pertinent to the understanding of complex systems such as metabolomics. Hence, we describe the relevance of scale-free networks in the understanding of metabolomics in this article. |
format | Online Article Text |
id | pubmed-5953857 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Biomedical Informatics |
record_format | MEDLINE/PubMed |
spelling | pubmed-59538572018-05-21 Scale-free networks in metabolomics Rajula, Hema Sekhar Reddy Mauri, Matteo Fanos, Vassilios Bioinformation Views Metabolomics is an expanding discipline in biology. It is the process of portraying the phenotype of a cell, tissue or species organism using a comprehensive set of metabolites. Therefore, it is of interest to understand complex systems such as metabolomics using a scale-free topology. Genetic networks and the World Wide Web (WWW) are described as networks with complex topology. Several large networks have vertex connectivity that goes beyond a scale-free power-law distribution. It is observed that (a) networks expand constantly by the addition of recent vertices, and (b) recent vertices attach preferentially to sites that are already well connected. Scalefree networks are determined with precision using vital features such as a structure, a disease and a patient. This is pertinent to the understanding of complex systems such as metabolomics. Hence, we describe the relevance of scale-free networks in the understanding of metabolomics in this article. Biomedical Informatics 2018-03-31 /pmc/articles/PMC5953857/ /pubmed/29785073 http://dx.doi.org/10.6026/97320630014140 Text en © 2018 Biomedical Informatics http://creativecommons.org/licenses/by/3.0/ This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License. |
spellingShingle | Views Rajula, Hema Sekhar Reddy Mauri, Matteo Fanos, Vassilios Scale-free networks in metabolomics |
title | Scale-free networks in metabolomics |
title_full | Scale-free networks in metabolomics |
title_fullStr | Scale-free networks in metabolomics |
title_full_unstemmed | Scale-free networks in metabolomics |
title_short | Scale-free networks in metabolomics |
title_sort | scale-free networks in metabolomics |
topic | Views |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5953857/ https://www.ncbi.nlm.nih.gov/pubmed/29785073 http://dx.doi.org/10.6026/97320630014140 |
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