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Local network-based measures to assess the inferability of different regulatory networks

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Local network-based measures to assess the inferability of different regulatory networks. / Emmert-Streib, F.; Altay, G.

In: IET Systems Biology, Vol. 4, No. 4, ISBEAT000004000004000277000001, 07.2010, p. 277-288.

Research output: Contribution to journalArticleScientificpeer-review

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Emmert-Streib, F & Altay, G 2010, 'Local network-based measures to assess the inferability of different regulatory networks', IET Systems Biology, vol. 4, no. 4, ISBEAT000004000004000277000001, pp. 277-288. https://doi.org/10.1049/iet-syb.2010.0028

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Emmert-Streib, F. ; Altay, G. / Local network-based measures to assess the inferability of different regulatory networks. In: IET Systems Biology. 2010 ; Vol. 4, No. 4. pp. 277-288.

Bibtex - Download

@article{14f5870db9ab4ae0bc430b2d45a66481,
title = "Local network-based measures to assess the inferability of different regulatory networks",
abstract = "The purpose of this study is to compare the inferability of various synthetic as well as real biological regulatory networks. In order to assess differences we apply local network-based measures. That means, instead of applying global measures, we investigate and assess an inference algorithm locally, on the level of individual edges and subnetworks. We demonstrate the behaviour of our local network-based measures with respect to different regulatory networks by conducting large-scale simulations. As inference algorithm we use exemplarily ARACNE. The results from our exploratory analysis allow us not only to gain new insights into the strength and weakness of an inference algorithm with respect to characteristics of different regulatory networks, but also to obtain information that could be used to design novel problem-specific statistical estimators. [Includes supplementary material]",
author = "F. Emmert-Streib and G. Altay",
year = "2010",
month = "7",
doi = "10.1049/iet-syb.2010.0028",
language = "English",
volume = "4",
pages = "277--288",
journal = "IET Systems Biology",
issn = "1751-8849",
publisher = "Institution of Engineering and Technology",
number = "4",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Local network-based measures to assess the inferability of different regulatory networks

AU - Emmert-Streib, F.

AU - Altay, G.

PY - 2010/7

Y1 - 2010/7

N2 - The purpose of this study is to compare the inferability of various synthetic as well as real biological regulatory networks. In order to assess differences we apply local network-based measures. That means, instead of applying global measures, we investigate and assess an inference algorithm locally, on the level of individual edges and subnetworks. We demonstrate the behaviour of our local network-based measures with respect to different regulatory networks by conducting large-scale simulations. As inference algorithm we use exemplarily ARACNE. The results from our exploratory analysis allow us not only to gain new insights into the strength and weakness of an inference algorithm with respect to characteristics of different regulatory networks, but also to obtain information that could be used to design novel problem-specific statistical estimators. [Includes supplementary material]

AB - The purpose of this study is to compare the inferability of various synthetic as well as real biological regulatory networks. In order to assess differences we apply local network-based measures. That means, instead of applying global measures, we investigate and assess an inference algorithm locally, on the level of individual edges and subnetworks. We demonstrate the behaviour of our local network-based measures with respect to different regulatory networks by conducting large-scale simulations. As inference algorithm we use exemplarily ARACNE. The results from our exploratory analysis allow us not only to gain new insights into the strength and weakness of an inference algorithm with respect to characteristics of different regulatory networks, but also to obtain information that could be used to design novel problem-specific statistical estimators. [Includes supplementary material]

UR - http://www.scopus.com/inward/record.url?scp=77954479475&partnerID=8YFLogxK

U2 - 10.1049/iet-syb.2010.0028

DO - 10.1049/iet-syb.2010.0028

M3 - Article

VL - 4

SP - 277

EP - 288

JO - IET Systems Biology

JF - IET Systems Biology

SN - 1751-8849

IS - 4

M1 - ISBEAT000004000004000277000001

ER -