Analysing omics data sets with weighted nodes networks (WNNets)

Abstract Current trends in biomedical research indicate data integration as a fundamental step towards precision medicine. In this context, network models allow representing and analysing complex biological processes. However, although effective in unveiling network properties, these models fail in...

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Autores principales:	Gabriele Tosadori, Dario Di Silvestre, Fausto Spoto, Pierluigi Mauri, Carlo Laudanna, Giovanni Scardoni
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/518522ca2324483886424696ee797ce2
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spelling	oai:doaj.org-article:518522ca2324483886424696ee797ce22021-12-02T16:08:07ZAnalysing omics data sets with weighted nodes networks (WNNets)10.1038/s41598-021-93699-32045-2322https://doaj.org/article/518522ca2324483886424696ee797ce22021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93699-3https://doaj.org/toc/2045-2322Abstract Current trends in biomedical research indicate data integration as a fundamental step towards precision medicine. In this context, network models allow representing and analysing complex biological processes. However, although effective in unveiling network properties, these models fail in considering the individual, biochemical variations occurring at molecular level. As a consequence, the analysis of these models partially loses its predictive power. To overcome these limitations, Weighted Nodes Networks (WNNets) were developed. WNNets allow to easily and effectively weigh nodes using experimental information from multiple conditions. In this study, the characteristics of WNNets were described and a proteomics data set was modelled and analysed. Results suggested that degree, an established centrality index, may offer a novel perspective about the functional role of nodes in WNNets. Indeed, degree allowed retrieving significant differences between experimental conditions, highlighting relevant proteins, and provided a novel interpretation for degree itself, opening new perspectives in experimental data modelling and analysis. Overall, WNNets may be used to model any high-throughput experimental data set requiring weighted nodes. Finally, improving the power of the analysis by using centralities such as betweenness may provide further biological insights and unveil novel, interesting characteristics of WNNets.Gabriele TosadoriDario Di SilvestreFausto SpotoPierluigi MauriCarlo LaudannaGiovanni ScardoniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gabriele Tosadori Dario Di Silvestre Fausto Spoto Pierluigi Mauri Carlo Laudanna Giovanni Scardoni Analysing omics data sets with weighted nodes networks (WNNets)
description	Abstract Current trends in biomedical research indicate data integration as a fundamental step towards precision medicine. In this context, network models allow representing and analysing complex biological processes. However, although effective in unveiling network properties, these models fail in considering the individual, biochemical variations occurring at molecular level. As a consequence, the analysis of these models partially loses its predictive power. To overcome these limitations, Weighted Nodes Networks (WNNets) were developed. WNNets allow to easily and effectively weigh nodes using experimental information from multiple conditions. In this study, the characteristics of WNNets were described and a proteomics data set was modelled and analysed. Results suggested that degree, an established centrality index, may offer a novel perspective about the functional role of nodes in WNNets. Indeed, degree allowed retrieving significant differences between experimental conditions, highlighting relevant proteins, and provided a novel interpretation for degree itself, opening new perspectives in experimental data modelling and analysis. Overall, WNNets may be used to model any high-throughput experimental data set requiring weighted nodes. Finally, improving the power of the analysis by using centralities such as betweenness may provide further biological insights and unveil novel, interesting characteristics of WNNets.
format	article
author	Gabriele Tosadori Dario Di Silvestre Fausto Spoto Pierluigi Mauri Carlo Laudanna Giovanni Scardoni
author_facet	Gabriele Tosadori Dario Di Silvestre Fausto Spoto Pierluigi Mauri Carlo Laudanna Giovanni Scardoni
author_sort	Gabriele Tosadori
title	Analysing omics data sets with weighted nodes networks (WNNets)
title_short	Analysing omics data sets with weighted nodes networks (WNNets)
title_full	Analysing omics data sets with weighted nodes networks (WNNets)
title_fullStr	Analysing omics data sets with weighted nodes networks (WNNets)
title_full_unstemmed	Analysing omics data sets with weighted nodes networks (WNNets)
title_sort	analysing omics data sets with weighted nodes networks (wnnets)
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/518522ca2324483886424696ee797ce2
work_keys_str_mv	AT gabrieletosadori analysingomicsdatasetswithweightednodesnetworkswnnets AT dariodisilvestre analysingomicsdatasetswithweightednodesnetworkswnnets AT faustospoto analysingomicsdatasetswithweightednodesnetworkswnnets AT pierluigimauri analysingomicsdatasetswithweightednodesnetworkswnnets AT carlolaudanna analysingomicsdatasetswithweightednodesnetworkswnnets AT giovanniscardoni analysingomicsdatasetswithweightednodesnetworkswnnets
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Analysing omics data sets with weighted nodes networks (WNNets)

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