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Influence of the experimental design of gene expression studies on the inference of gene regulatory networks: Environmental factors

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Influence of the experimental design of gene expression studies on the inference of gene regulatory networks : Environmental factors. / Emmert-Streib, Frank.

In: PeerJ, Vol. 2013, No. 1, e10, 2013.

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@article{d6ea3a8e6bdc491b95897611a309b231,
title = "Influence of the experimental design of gene expression studies on the inference of gene regulatory networks: Environmental factors",
abstract = "The inference of gene regulatory networks gained within recent years a considerable interest in the biology and biomedical community. The purpose of this paper is to investigate the influence that environmental conditions can exhibit on the inference performance of network inference algorithms. Specifically, we study five network inference methods, Aracne, BC3NET, CLR, C3NET and MRNET, and compare the results for three different conditions: (I) observational gene expression data: normal environmental condition, (II) interventional gene expression data: growth in rich media, (III) interventional gene expression data: normal environmental condition interrupted by a positive spike-in stimulation. Overall, we find that different statistical inference methods lead to comparable, but condition-specific results. Further, our results suggest that non-steady-state data enhance the inferability of regulatory networks.",
keywords = "Experimental design, Gene expression data, Gene regulatory networks, Interventional data, Statistical network inference",
author = "Frank Emmert-Streib",
year = "2013",
doi = "10.7717/peerj.10",
language = "English",
volume = "2013",
journal = "PeerJ",
issn = "2167-8359",
publisher = "PeerJ",
number = "1",

}

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TY - JOUR

T1 - Influence of the experimental design of gene expression studies on the inference of gene regulatory networks

T2 - Environmental factors

AU - Emmert-Streib, Frank

PY - 2013

Y1 - 2013

N2 - The inference of gene regulatory networks gained within recent years a considerable interest in the biology and biomedical community. The purpose of this paper is to investigate the influence that environmental conditions can exhibit on the inference performance of network inference algorithms. Specifically, we study five network inference methods, Aracne, BC3NET, CLR, C3NET and MRNET, and compare the results for three different conditions: (I) observational gene expression data: normal environmental condition, (II) interventional gene expression data: growth in rich media, (III) interventional gene expression data: normal environmental condition interrupted by a positive spike-in stimulation. Overall, we find that different statistical inference methods lead to comparable, but condition-specific results. Further, our results suggest that non-steady-state data enhance the inferability of regulatory networks.

AB - The inference of gene regulatory networks gained within recent years a considerable interest in the biology and biomedical community. The purpose of this paper is to investigate the influence that environmental conditions can exhibit on the inference performance of network inference algorithms. Specifically, we study five network inference methods, Aracne, BC3NET, CLR, C3NET and MRNET, and compare the results for three different conditions: (I) observational gene expression data: normal environmental condition, (II) interventional gene expression data: growth in rich media, (III) interventional gene expression data: normal environmental condition interrupted by a positive spike-in stimulation. Overall, we find that different statistical inference methods lead to comparable, but condition-specific results. Further, our results suggest that non-steady-state data enhance the inferability of regulatory networks.

KW - Experimental design

KW - Gene expression data

KW - Gene regulatory networks

KW - Interventional data

KW - Statistical network inference

UR - http://www.scopus.com/inward/record.url?scp=84877135982&partnerID=8YFLogxK

U2 - 10.7717/peerj.10

DO - 10.7717/peerj.10

M3 - Article

VL - 2013

JO - PeerJ

JF - PeerJ

SN - 2167-8359

IS - 1

M1 - e10

ER -