A computational approach to chemical etiologies of diabetes

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A computational approach to chemical etiologies of diabetes. / Audouze, Karine Marie Laure; Brunak, Søren; Grandjean, Philippe.

In: Scientific Reports, Vol. 3, 2712, 19.09.2013, p. 2712.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Audouze, KML, Brunak, S & Grandjean, P 2013, 'A computational approach to chemical etiologies of diabetes', Scientific Reports, vol. 3, 2712, pp. 2712. https://doi.org/10.1038/srep02712

APA

Audouze, K. M. L., Brunak, S., & Grandjean, P. (2013). A computational approach to chemical etiologies of diabetes. Scientific Reports, 3, 2712. [2712]. https://doi.org/10.1038/srep02712

Vancouver

Audouze KML, Brunak S, Grandjean P. A computational approach to chemical etiologies of diabetes. Scientific Reports. 2013 Sep 19;3:2712. 2712. https://doi.org/10.1038/srep02712

Author

Audouze, Karine Marie Laure ; Brunak, Søren ; Grandjean, Philippe. / A computational approach to chemical etiologies of diabetes. In: Scientific Reports. 2013 ; Vol. 3. pp. 2712.

Bibtex

@article{9ec1db1b5aa440c48a64ba6a18804609,
title = "A computational approach to chemical etiologies of diabetes",
abstract = "Computational meta-analysis can link environmental chemicals to genes and proteins involved in human diseases, thereby elucidating possible etiologies and pathogeneses of non-communicable diseases. We used an integrated computational systems biology approach to examine possible pathogenetic linkages in type 2 diabetes (T2D) through genome-wide associations, disease similarities, and published empirical evidence. Ten environmental chemicals were found to be potentially linked to T2D, the highest scores were observed for arsenic, 2,3,7,8-tetrachlorodibenzo-p-dioxin, hexachlorobenzene, and perfluorooctanoic acid. For these substances we integrated disease and pathway annotations on top of protein interactions to reveal possible pathogenetic pathways that deserve empirical testing. The approach is general and can address other public health concerns in addition to identifying diabetogenic chemicals, and offers thus promising guidance for future research in regard to the etiology and pathogenesis of complex diseases.",
author = "Audouze, {Karine Marie Laure} and S{\o}ren Brunak and Philippe Grandjean",
year = "2013",
month = sep,
day = "19",
doi = "10.1038/srep02712",
language = "English",
volume = "3",
pages = "2712",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "nature publishing group",

}

RIS

TY - JOUR

T1 - A computational approach to chemical etiologies of diabetes

AU - Audouze, Karine Marie Laure

AU - Brunak, Søren

AU - Grandjean, Philippe

PY - 2013/9/19

Y1 - 2013/9/19

N2 - Computational meta-analysis can link environmental chemicals to genes and proteins involved in human diseases, thereby elucidating possible etiologies and pathogeneses of non-communicable diseases. We used an integrated computational systems biology approach to examine possible pathogenetic linkages in type 2 diabetes (T2D) through genome-wide associations, disease similarities, and published empirical evidence. Ten environmental chemicals were found to be potentially linked to T2D, the highest scores were observed for arsenic, 2,3,7,8-tetrachlorodibenzo-p-dioxin, hexachlorobenzene, and perfluorooctanoic acid. For these substances we integrated disease and pathway annotations on top of protein interactions to reveal possible pathogenetic pathways that deserve empirical testing. The approach is general and can address other public health concerns in addition to identifying diabetogenic chemicals, and offers thus promising guidance for future research in regard to the etiology and pathogenesis of complex diseases.

AB - Computational meta-analysis can link environmental chemicals to genes and proteins involved in human diseases, thereby elucidating possible etiologies and pathogeneses of non-communicable diseases. We used an integrated computational systems biology approach to examine possible pathogenetic linkages in type 2 diabetes (T2D) through genome-wide associations, disease similarities, and published empirical evidence. Ten environmental chemicals were found to be potentially linked to T2D, the highest scores were observed for arsenic, 2,3,7,8-tetrachlorodibenzo-p-dioxin, hexachlorobenzene, and perfluorooctanoic acid. For these substances we integrated disease and pathway annotations on top of protein interactions to reveal possible pathogenetic pathways that deserve empirical testing. The approach is general and can address other public health concerns in addition to identifying diabetogenic chemicals, and offers thus promising guidance for future research in regard to the etiology and pathogenesis of complex diseases.

U2 - 10.1038/srep02712

DO - 10.1038/srep02712

M3 - Journal article

C2 - 24048418

VL - 3

SP - 2712

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

M1 - 2712

ER -

ID: 58017398