Discriminating different classes of biological networks by analyzing the graphs spectra distribution.

The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies pres...

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Autores principales:	Daniel Yasumasa Takahashi, João Ricardo Sato, Carlos Eduardo Ferreira, André Fujita
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/776d09056977474b8acaa630fe5503a0
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spelling	oai:doaj.org-article:776d09056977474b8acaa630fe5503a02021-11-18T08:04:35ZDiscriminating different classes of biological networks by analyzing the graphs spectra distribution.1932-620310.1371/journal.pone.0049949https://doaj.org/article/776d09056977474b8acaa630fe5503a02012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23284629/?tool=EBIhttps://doaj.org/toc/1932-6203The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e.g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them to (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed.Daniel Yasumasa TakahashiJoão Ricardo SatoCarlos Eduardo FerreiraAndré FujitaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 12, p e49949 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Daniel Yasumasa Takahashi João Ricardo Sato Carlos Eduardo Ferreira André Fujita Discriminating different classes of biological networks by analyzing the graphs spectra distribution.
description	The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e.g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them to (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed.
format	article
author	Daniel Yasumasa Takahashi João Ricardo Sato Carlos Eduardo Ferreira André Fujita
author_facet	Daniel Yasumasa Takahashi João Ricardo Sato Carlos Eduardo Ferreira André Fujita
author_sort	Daniel Yasumasa Takahashi
title	Discriminating different classes of biological networks by analyzing the graphs spectra distribution.
title_short	Discriminating different classes of biological networks by analyzing the graphs spectra distribution.
title_full	Discriminating different classes of biological networks by analyzing the graphs spectra distribution.
title_fullStr	Discriminating different classes of biological networks by analyzing the graphs spectra distribution.
title_full_unstemmed	Discriminating different classes of biological networks by analyzing the graphs spectra distribution.
title_sort	discriminating different classes of biological networks by analyzing the graphs spectra distribution.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/776d09056977474b8acaa630fe5503a0
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Discriminating different classes of biological networks by analyzing the graphs spectra distribution.

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