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    Rights statement: © 2011 Xiao et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the generalized singular value decomposition

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Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the generalized singular value decomposition. / Xiao, Xiaolin; Dawson, Neil; MacIntyre, Lynsey et al.
In: BMC Systems Biology, Vol. 5, 72, 16.05.2011.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Xiao X, Dawson N, MacIntyre L, Morris B, Pratt J, Watson D et al. Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the generalized singular value decomposition. BMC Systems Biology. 2011 May 16;5:72. doi: 10.1186/1752-0509-5-72

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@article{9c9ffbf9e00d4875bb88d9d0f562d0f7,
title = "Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the generalized singular value decomposition",
abstract = "The quantification of experimentally-induced alterations in biological pathways remains a major challenge in systems biology. One example of this is the quantitative characterization of alterations in defined, established metabolic pathways from complex metabolomic data. At present, the disruption of a given metabolic pathway is inferred from metabolomic data by observing an alteration in the level of one or more individual metabolites present within that pathway. Not only is this approach open to subjectivity, as metabolites participate in multiple pathways, but it also ignores useful information available through the pairwise correlations between metabolites. This extra information may be incorporated using a higher-level approach that looks for alterations between a pair of correlation networks. In this way experimentally-induced alterations in metabolic pathways can be quantitatively defined by characterizing group differences in metabolite clustering. Taking this approach increases the objectivity of interpreting alterations in metabolic pathways from metabolomic data. ",
author = "Xiaolin Xiao and Neil Dawson and Lynsey MacIntyre and Brian Morris and Judith Pratt and David Watson and Desmond Higham",
note = "{\textcopyright} 2011 Xiao et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.",
year = "2011",
month = may,
day = "16",
doi = "10.1186/1752-0509-5-72",
language = "English",
volume = "5",
journal = "BMC Systems Biology",
publisher = "BioMed Central",

}

RIS

TY - JOUR

T1 - Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the generalized singular value decomposition

AU - Xiao, Xiaolin

AU - Dawson, Neil

AU - MacIntyre, Lynsey

AU - Morris, Brian

AU - Pratt, Judith

AU - Watson, David

AU - Higham, Desmond

N1 - © 2011 Xiao et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PY - 2011/5/16

Y1 - 2011/5/16

N2 - The quantification of experimentally-induced alterations in biological pathways remains a major challenge in systems biology. One example of this is the quantitative characterization of alterations in defined, established metabolic pathways from complex metabolomic data. At present, the disruption of a given metabolic pathway is inferred from metabolomic data by observing an alteration in the level of one or more individual metabolites present within that pathway. Not only is this approach open to subjectivity, as metabolites participate in multiple pathways, but it also ignores useful information available through the pairwise correlations between metabolites. This extra information may be incorporated using a higher-level approach that looks for alterations between a pair of correlation networks. In this way experimentally-induced alterations in metabolic pathways can be quantitatively defined by characterizing group differences in metabolite clustering. Taking this approach increases the objectivity of interpreting alterations in metabolic pathways from metabolomic data.

AB - The quantification of experimentally-induced alterations in biological pathways remains a major challenge in systems biology. One example of this is the quantitative characterization of alterations in defined, established metabolic pathways from complex metabolomic data. At present, the disruption of a given metabolic pathway is inferred from metabolomic data by observing an alteration in the level of one or more individual metabolites present within that pathway. Not only is this approach open to subjectivity, as metabolites participate in multiple pathways, but it also ignores useful information available through the pairwise correlations between metabolites. This extra information may be incorporated using a higher-level approach that looks for alterations between a pair of correlation networks. In this way experimentally-induced alterations in metabolic pathways can be quantitatively defined by characterizing group differences in metabolite clustering. Taking this approach increases the objectivity of interpreting alterations in metabolic pathways from metabolomic data.

U2 - 10.1186/1752-0509-5-72

DO - 10.1186/1752-0509-5-72

M3 - Journal article

VL - 5

JO - BMC Systems Biology

JF - BMC Systems Biology

M1 - 72

ER -