Equal opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs.

Biological network data, such as metabolic-, signaling- or physical interaction graphs of proteins are increasingly available in public repositories for important species. Tools for the quantitative analysis of these networks are being developed today. Protein network-based drug target identificatio...

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Autores principales:	Dániel Bánky, Gábor Iván, Vince Grolmusz
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8fa3e19e96ca4d319d2c10233a0b3fda
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spelling	oai:doaj.org-article:8fa3e19e96ca4d319d2c10233a0b3fda2021-11-18T07:59:42ZEqual opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs.1932-620310.1371/journal.pone.0054204https://doaj.org/article/8fa3e19e96ca4d319d2c10233a0b3fda2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23382878/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Biological network data, such as metabolic-, signaling- or physical interaction graphs of proteins are increasingly available in public repositories for important species. Tools for the quantitative analysis of these networks are being developed today. Protein network-based drug target identification methods usually return protein hubs with large degrees in the networks as potentially important targets. Some known, important protein targets, however, are not hubs at all, and perturbing protein hubs in these networks may have several unwanted physiological effects, due to their interaction with numerous partners. Here, we show a novel method applicable in networks with directed edges (such as metabolic networks) that compensates for the low degree (non-hub) vertices in the network, and identifies important nodes, regardless of their hub properties. Our method computes the PageRank for the nodes of the network, and divides the PageRank by the in-degree (i.e., the number of incoming edges) of the node. This quotient is the same in all nodes in an undirected graph (even for large- and low-degree nodes, that is, for hubs and non-hubs as well), but may differ significantly from node to node in directed graphs. We suggest to assign importance to non-hub nodes with large PageRank/in-degree quotient. Consequently, our method gives high scores to nodes with large PageRank, relative to their degrees: therefore non-hub important nodes can easily be identified in large networks. We demonstrate that these relatively high PageRank scores have biological relevance: the method correctly finds numerous already validated drug targets in distinct organisms (Mycobacterium tuberculosis, Plasmodium falciparum and MRSA Staphylococcus aureus), and consequently, it may suggest new possible protein targets as well. Additionally, our scoring method was not chosen arbitrarily: its value for all nodes of all undirected graphs is constant; therefore its high value captures importance in the directed edge structure of the graph.Dániel BánkyGábor IvánVince GrolmuszPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 1, p e54204 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dániel Bánky Gábor Iván Vince Grolmusz Equal opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs.
description	Biological network data, such as metabolic-, signaling- or physical interaction graphs of proteins are increasingly available in public repositories for important species. Tools for the quantitative analysis of these networks are being developed today. Protein network-based drug target identification methods usually return protein hubs with large degrees in the networks as potentially important targets. Some known, important protein targets, however, are not hubs at all, and perturbing protein hubs in these networks may have several unwanted physiological effects, due to their interaction with numerous partners. Here, we show a novel method applicable in networks with directed edges (such as metabolic networks) that compensates for the low degree (non-hub) vertices in the network, and identifies important nodes, regardless of their hub properties. Our method computes the PageRank for the nodes of the network, and divides the PageRank by the in-degree (i.e., the number of incoming edges) of the node. This quotient is the same in all nodes in an undirected graph (even for large- and low-degree nodes, that is, for hubs and non-hubs as well), but may differ significantly from node to node in directed graphs. We suggest to assign importance to non-hub nodes with large PageRank/in-degree quotient. Consequently, our method gives high scores to nodes with large PageRank, relative to their degrees: therefore non-hub important nodes can easily be identified in large networks. We demonstrate that these relatively high PageRank scores have biological relevance: the method correctly finds numerous already validated drug targets in distinct organisms (Mycobacterium tuberculosis, Plasmodium falciparum and MRSA Staphylococcus aureus), and consequently, it may suggest new possible protein targets as well. Additionally, our scoring method was not chosen arbitrarily: its value for all nodes of all undirected graphs is constant; therefore its high value captures importance in the directed edge structure of the graph.
format	article
author	Dániel Bánky Gábor Iván Vince Grolmusz
author_facet	Dániel Bánky Gábor Iván Vince Grolmusz
author_sort	Dániel Bánky
title	Equal opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs.
title_short	Equal opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs.
title_full	Equal opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs.
title_fullStr	Equal opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs.
title_full_unstemmed	Equal opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs.
title_sort	equal opportunity for low-degree network nodes: a pagerank-based method for protein target identification in metabolic graphs.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/8fa3e19e96ca4d319d2c10233a0b3fda
work_keys_str_mv	AT danielbanky equalopportunityforlowdegreenetworknodesapagerankbasedmethodforproteintargetidentificationinmetabolicgraphs AT gaborivan equalopportunityforlowdegreenetworknodesapagerankbasedmethodforproteintargetidentificationinmetabolicgraphs AT vincegrolmusz equalopportunityforlowdegreenetworknodesapagerankbasedmethodforproteintargetidentificationinmetabolicgraphs
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Equal opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs.

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