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Evolutionary approaches for the reverse-engineering of gene regulatory networks: a study on a biologically realistic dataset.

Abstract Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interactions may be modelled by a Bayesian Network (BN) that represents the presence of direct interactions from regulators to regulees by conditional probability distributions. We used enhanced evolutionary algorithms to stochastically evolve a set of candidate BN structures and found the model that best fits data without prior knowledge.
PMID
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Authors

Mayor MeshTerms

Biological Evolution

Evolution, Molecular

Models, Genetic

Keywords
Journal Title bmc bioinformatics
Publication Year Start
%A Auliac, C?dric; Frouin, Vincent; Gidrol, Xavier; d'Alch?-Buc, Florence
%T Evolutionary approaches for the reverse-engineering of gene regulatory networks: a study on a biologically realistic dataset.
%J BMC bioinformatics, vol. 9, p. 91
%D 02/2008
%V 9
%M eng
%B Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interactions may be modelled by a Bayesian Network (BN) that represents the presence of direct interactions from regulators to regulees by conditional probability distributions. We used enhanced evolutionary algorithms to stochastically evolve a set of candidate BN structures and found the model that best fits data without prior knowledge.
%K Biological Evolution, Computer Simulation, Evolution, Molecular, Gene Expression Regulation, Genetic Variation, Models, Genetic, Signal Transduction, Transcription Factors
%P 91
%Y 10.1186/1471-2105-9-91
%W PHY
%G AUTHOR
%R 2008........9...91A

@Article{Auliac2008,
author="Auliac, C{\'e}dric
and Frouin, Vincent
and Gidrol, Xavier
and d'Alch{\'e}-Buc, Florence",
title="Evolutionary approaches for the reverse-engineering of gene regulatory networks: a study on a biologically realistic dataset.",
journal="BMC bioinformatics",
year="2008",
month="Feb",
day="08",
volume="9",
pages="91",
keywords="Biological Evolution",
keywords="Computer Simulation",
keywords="Evolution, Molecular",
keywords="Gene Expression Regulation",
keywords="Genetic Variation",
keywords="Models, Genetic",
keywords="Signal Transduction",
keywords="Transcription Factors",
abstract="Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interactions may be modelled by a Bayesian Network (BN) that represents the presence of direct interactions from regulators to regulees by conditional probability distributions. We used enhanced evolutionary algorithms to stochastically evolve a set of candidate BN structures and found the model that best fits data without prior knowledge.",
issn="1471-2105",
doi="10.1186/1471-2105-9-91",
url="http://www.ncbi.nlm.nih.gov/pubmed/18261218",
language="eng"
}

%0 Journal Article
%T Evolutionary approaches for the reverse-engineering of gene regulatory networks: a study on a biologically realistic dataset.
%A Auliac, C?dric
%A Frouin, Vincent
%A Gidrol, Xavier
%A d'Alch?-Buc, Florence
%J BMC bioinformatics
%D 2008
%8 February 08
%V 9
%@ 1471-2105
%G eng
%F Auliac2008
%X Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interactions may be modelled by a Bayesian Network (BN) that represents the presence of direct interactions from regulators to regulees by conditional probability distributions. We used enhanced evolutionary algorithms to stochastically evolve a set of candidate BN structures and found the model that best fits data without prior knowledge.
%K Biological Evolution
%K Computer Simulation
%K Evolution, Molecular
%K Gene Expression Regulation
%K Genetic Variation
%K Models, Genetic
%K Signal Transduction
%K Transcription Factors
%U http://dx.doi.org/10.1186/1471-2105-9-91
%U http://www.ncbi.nlm.nih.gov/pubmed/18261218
%P 91

PT Journal
AU Auliac, C
   Frouin, V
   Gidrol, X
   d'Alch?-Buc, F
TI Evolutionary approaches for the reverse-engineering of gene regulatory networks: a study on a biologically realistic dataset.
SO BMC bioinformatics
JI BMC Bioinformatics
PD 02
PY 2008
BP 91
VL 9
DI 10.1186/1471-2105-9-91
LA eng
DE Biological Evolution; Computer Simulation; Evolution, Molecular; Gene Expression Regulation; Genetic Variation; Models, Genetic; Signal Transduction; Transcription Factors
AB Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interactions may be modelled by a Bayesian Network (BN) that represents the presence of direct interactions from regulators to regulees by conditional probability distributions. We used enhanced evolutionary algorithms to stochastically evolve a set of candidate BN structures and found the model that best fits data without prior knowledge.
ER

PMID- 18261218
OWN - NLM
STAT- MEDLINE
DA  - 20080404
DCOM- 20080428
LR  - 20140904
IS  - 1471-2105 (Electronic)
IS  - 1471-2105 (Linking)
VI  - 9
DP  - 2008
TI  - Evolutionary approaches for the reverse-engineering of gene regulatory networks: 
      a study on a biologically realistic dataset.
PG  - 91
LID - 10.1186/1471-2105-9-91 [doi]
AB  - BACKGROUND: Inferring gene regulatory networks from data requires the development
      of algorithms devoted to structure extraction. When only static data are
      available, gene interactions may be modelled by a Bayesian Network (BN) that
      represents the presence of direct interactions from regulators to regulees by
      conditional probability distributions. We used enhanced evolutionary algorithms
      to stochastically evolve a set of candidate BN structures and found the model
      that best fits data without prior knowledge. RESULTS: We proposed various
      evolutionary strategies suitable for the task and tested our choices using
      simulated data drawn from a given bio-realistic network of 35 nodes, the
      so-called insulin network, which has been used in the literature for
      benchmarking. We assessed the inferred models against this reference to obtain
      statistical performance results. We then compared performances of evolutionary
      algorithms using two kinds of recombination operators that operate at different
      scales in the graphs. We introduced a niching strategy that reinforces diversity 
      through the population and avoided trapping of the algorithm in one local minimum
      in the early steps of learning. We show the limited effect of the mutation
      operator when niching is applied. Finally, we compared our best evolutionary
      approach with various well known learning algorithms (MCMC, K2, greedy search,
      TPDA, MMHC) devoted to BN structure learning. CONCLUSION: We studied the
      behaviour of an evolutionary approach enhanced by niching for the learning of
      gene regulatory networks with BN. We show that this approach outperforms
      classical structure learning methods in elucidating the original model. These
      results were obtained for the learning of a bio-realistic network and, more
      importantly, on various small datasets. This is a suitable approach for learning 
      transcriptional regulatory networks from real datasets without prior knowledge.
FAU - Auliac, Cedric
AU  - Auliac C
AD  - Laboratoire Informatique, Biologie Integrative et Systemes Complexes (IBISC),
      Universite d'Evry-Val d'Essonne, Evry, France. [email protected]
FAU - Frouin, Vincent
AU  - Frouin V
FAU - Gidrol, Xavier
AU  - Gidrol X
FAU - d'Alche-Buc, Florence
AU  - d'Alche-Buc F
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20080208
PL  - England
TA  - BMC Bioinformatics
JT  - BMC bioinformatics
JID - 100965194
RN  - 0 (Transcription Factors)
SB  - IM
MH  - *Biological Evolution
MH  - Computer Simulation
MH  - *Evolution, Molecular
MH  - Gene Expression Regulation/*genetics
MH  - Genetic Variation/*genetics
MH  - *Models, Genetic
MH  - Signal Transduction/*genetics
MH  - Transcription Factors/*genetics
PMC - PMC2335304
OID - NLM: PMC2335304
EDAT- 2008/02/12 09:00
MHDA- 2008/04/29 09:00
CRDT- 2008/02/12 09:00
PHST- 2007/08/09 [received]
PHST- 2008/02/08 [accepted]
PHST- 2008/02/08 [aheadofprint]
AID - 1471-2105-9-91 [pii]
AID - 10.1186/1471-2105-9-91 [doi]
PST - epublish
SO  - BMC Bioinformatics. 2008 Feb 8;9:91. doi: 10.1186/1471-2105-9-91.
TY  - JOUR
AU  - Auliac, C?dric
AU  - Frouin, Vincent
AU  - Gidrol, Xavier
AU  - d'Alch?-Buc, Florence
PY  - 2008/02/08
TI  - Evolutionary approaches for the reverse-engineering of gene regulatory networks: a study on a biologically realistic dataset.
T2  - BMC Bioinformatics
JO  - BMC bioinformatics
SP  - 91
VL  - 9
KW  - Biological Evolution
KW  - Computer Simulation
KW  - Evolution, Molecular
KW  - Gene Expression Regulation
KW  - Genetic Variation
KW  - Models, Genetic
KW  - Signal Transduction
KW  - Transcription Factors
N2  - Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interactions may be modelled by a Bayesian Network (BN) that represents the presence of direct interactions from regulators to regulees by conditional probability distributions. We used enhanced evolutionary algorithms to stochastically evolve a set of candidate BN structures and found the model that best fits data without prior knowledge.
SN  - 1471-2105
UR  - http://dx.doi.org/10.1186/1471-2105-9-91
UR  - http://www.ncbi.nlm.nih.gov/pubmed/18261218
ID  - Auliac2008
ER  - 
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