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Revealing differences in gene network inference algorithms on the network level by ensemble methods

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Revealing differences in gene network inference algorithms on the network level by ensemble methods. / Altay, Gökmen; Emmert-Streib, Frank.

In: Bioinformatics, Vol. 26, No. 14, btq259, 25.05.2010, p. 1738-1744.

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@article{be5c07cfecc1437c9898284c98e29eff,
title = "Revealing differences in gene network inference algorithms on the network level by ensemble methods",
abstract = "Motivation: The inference of regulatory networks from large-scale expression data holds great promise because of the potentially causal interpretation of these networks. However, due to the difficulty to establish reliable methods based on observational data there is so far only incomplete knowledge about possibilities and limitations of such inference methods in this context. Results: In this article, we conduct a statistical analysis investigating differences and similarities of four network inference algorithms, ARACNE, CLR, MRNET and RN, with respect to local network-based measures. We employ ensemble methods allowing to assess the inferability down to the level of individual edges. Our analysis reveals the bias of these inference methods with respect to the inference of various network components and, hence, provides guidance in the interpretation of inferred regulatory networks from expression data. Further, as application we predict the total number of regulatory interactions in human B cells and hypothesize about the role of Myc and its targets regarding molecular information processing.",
author = "G{\"o}kmen Altay and Frank Emmert-Streib",
year = "2010",
month = "5",
day = "25",
doi = "10.1093/bioinformatics/btq259",
language = "English",
volume = "26",
pages = "1738--1744",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "14",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Revealing differences in gene network inference algorithms on the network level by ensemble methods

AU - Altay, Gökmen

AU - Emmert-Streib, Frank

PY - 2010/5/25

Y1 - 2010/5/25

N2 - Motivation: The inference of regulatory networks from large-scale expression data holds great promise because of the potentially causal interpretation of these networks. However, due to the difficulty to establish reliable methods based on observational data there is so far only incomplete knowledge about possibilities and limitations of such inference methods in this context. Results: In this article, we conduct a statistical analysis investigating differences and similarities of four network inference algorithms, ARACNE, CLR, MRNET and RN, with respect to local network-based measures. We employ ensemble methods allowing to assess the inferability down to the level of individual edges. Our analysis reveals the bias of these inference methods with respect to the inference of various network components and, hence, provides guidance in the interpretation of inferred regulatory networks from expression data. Further, as application we predict the total number of regulatory interactions in human B cells and hypothesize about the role of Myc and its targets regarding molecular information processing.

AB - Motivation: The inference of regulatory networks from large-scale expression data holds great promise because of the potentially causal interpretation of these networks. However, due to the difficulty to establish reliable methods based on observational data there is so far only incomplete knowledge about possibilities and limitations of such inference methods in this context. Results: In this article, we conduct a statistical analysis investigating differences and similarities of four network inference algorithms, ARACNE, CLR, MRNET and RN, with respect to local network-based measures. We employ ensemble methods allowing to assess the inferability down to the level of individual edges. Our analysis reveals the bias of these inference methods with respect to the inference of various network components and, hence, provides guidance in the interpretation of inferred regulatory networks from expression data. Further, as application we predict the total number of regulatory interactions in human B cells and hypothesize about the role of Myc and its targets regarding molecular information processing.

UR - http://www.scopus.com/inward/record.url?scp=77954484005&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btq259

DO - 10.1093/bioinformatics/btq259

M3 - Article

VL - 26

SP - 1738

EP - 1744

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 14

M1 - btq259

ER -