Use of data-biased random walks on graphs for the retrieval of context-specific networks from genomic data.

Extracting network-based functional relationships within genomic datasets is an important challenge in the computational analysis of large-scale data. Although many methods, both public and commercial, have been developed, the problem of identifying networks of interactions that are most relevant to...

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Autores principales:	Kakajan Komurov, Michael A White, Prahlad T Ram
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/70f9f1b749904e2ab617cbcc68214f84
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spelling	oai:doaj.org-article:70f9f1b749904e2ab617cbcc68214f842021-11-18T05:49:23ZUse of data-biased random walks on graphs for the retrieval of context-specific networks from genomic data.1553-734X1553-735810.1371/journal.pcbi.1000889https://doaj.org/article/70f9f1b749904e2ab617cbcc68214f842010-08-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20808879/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Extracting network-based functional relationships within genomic datasets is an important challenge in the computational analysis of large-scale data. Although many methods, both public and commercial, have been developed, the problem of identifying networks of interactions that are most relevant to the given input data still remains an open issue. Here, we have leveraged the method of random walks on graphs as a powerful platform for scoring network components based on simultaneous assessment of the experimental data as well as local network connectivity. Using this method, NetWalk, we can calculate distribution of Edge Flux values associated with each interaction in the network, which reflects the relevance of interactions based on the experimental data. We show that network-based analyses of genomic data are simpler and more accurate using NetWalk than with some of the currently employed methods. We also present NetWalk analysis of microarray gene expression data from MCF7 cells exposed to different doses of doxorubicin, which reveals a switch-like pattern in the p53 regulated network in cell cycle arrest and apoptosis. Our analyses demonstrate the use of NetWalk as a valuable tool in generating high-confidence hypotheses from high-content genomic data.Kakajan KomurovMichael A WhitePrahlad T RamPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 6, Iss 8 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Kakajan Komurov Michael A White Prahlad T Ram Use of data-biased random walks on graphs for the retrieval of context-specific networks from genomic data.
description	Extracting network-based functional relationships within genomic datasets is an important challenge in the computational analysis of large-scale data. Although many methods, both public and commercial, have been developed, the problem of identifying networks of interactions that are most relevant to the given input data still remains an open issue. Here, we have leveraged the method of random walks on graphs as a powerful platform for scoring network components based on simultaneous assessment of the experimental data as well as local network connectivity. Using this method, NetWalk, we can calculate distribution of Edge Flux values associated with each interaction in the network, which reflects the relevance of interactions based on the experimental data. We show that network-based analyses of genomic data are simpler and more accurate using NetWalk than with some of the currently employed methods. We also present NetWalk analysis of microarray gene expression data from MCF7 cells exposed to different doses of doxorubicin, which reveals a switch-like pattern in the p53 regulated network in cell cycle arrest and apoptosis. Our analyses demonstrate the use of NetWalk as a valuable tool in generating high-confidence hypotheses from high-content genomic data.
format	article
author	Kakajan Komurov Michael A White Prahlad T Ram
author_facet	Kakajan Komurov Michael A White Prahlad T Ram
author_sort	Kakajan Komurov
title	Use of data-biased random walks on graphs for the retrieval of context-specific networks from genomic data.
title_short	Use of data-biased random walks on graphs for the retrieval of context-specific networks from genomic data.
title_full	Use of data-biased random walks on graphs for the retrieval of context-specific networks from genomic data.
title_fullStr	Use of data-biased random walks on graphs for the retrieval of context-specific networks from genomic data.
title_full_unstemmed	Use of data-biased random walks on graphs for the retrieval of context-specific networks from genomic data.
title_sort	use of data-biased random walks on graphs for the retrieval of context-specific networks from genomic data.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/70f9f1b749904e2ab617cbcc68214f84
work_keys_str_mv	AT kakajankomurov useofdatabiasedrandomwalksongraphsfortheretrievalofcontextspecificnetworksfromgenomicdata AT michaelawhite useofdatabiasedrandomwalksongraphsfortheretrievalofcontextspecificnetworksfromgenomicdata AT prahladtram useofdatabiasedrandomwalksongraphsfortheretrievalofcontextspecificnetworksfromgenomicdata
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Use of data-biased random walks on graphs for the retrieval of context-specific networks from genomic data.

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