Tampere University of Technology

TUTCRIS Research Portal

Inferring the conservative causal core of gene regulatory networks

Research output: Contribution to journalArticleScientificpeer-review

Standard

Inferring the conservative causal core of gene regulatory networks. / Altay, Gökmen; Emmert-Streib, Frank.

In: BMC Systems Biology, Vol. 4, 132, 28.09.2010.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

APA

Vancouver

Author

Bibtex - Download

@article{46d851d501cb4d86a18a1ec34542d3a2,
title = "Inferring the conservative causal core of gene regulatory networks",
abstract = "Background: Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically.Results: In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently.Conclusions: For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.",
author = "G{\"o}kmen Altay and Frank Emmert-Streib",
year = "2010",
month = "9",
day = "28",
doi = "10.1186/1752-0509-4-132",
language = "English",
volume = "4",
journal = "BMC Systems Biology",
issn = "1752-0509",
publisher = "Springer Verlag",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Inferring the conservative causal core of gene regulatory networks

AU - Altay, Gökmen

AU - Emmert-Streib, Frank

PY - 2010/9/28

Y1 - 2010/9/28

N2 - Background: Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically.Results: In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently.Conclusions: For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

AB - Background: Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically.Results: In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently.Conclusions: For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

UR - http://www.scopus.com/inward/record.url?scp=77957141016&partnerID=8YFLogxK

U2 - 10.1186/1752-0509-4-132

DO - 10.1186/1752-0509-4-132

M3 - Article

VL - 4

JO - BMC Systems Biology

JF - BMC Systems Biology

SN - 1752-0509

M1 - 132

ER -