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

BMC Bioinformatics

Volumn 9, Issue , 2008, Pages

  Auliac, Cédric ^a,b   Frouin, Vincent ^b   Gidrol, Xavier ^b   d'Alché Buc, Florence ^a  

^a UNIVERSITY OF EVRY   (France)

^b DIF   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CONDITIONAL PROBABILITY DISTRIBUTIONS; EVOLUTIONARY APPROACH; EVOLUTIONARY STRATEGIES; GENE REGULATORY NETWORKS; RECOMBINATION OPERATORS; STATISTICAL PERFORMANCE; STRUCTURE EXTRACTION; TRANSCRIPTIONAL REGULATORY NETWORKS;

BAYESIAN NETWORKS; EVOLUTIONARY ALGORITHMS; LARGE SCALE SYSTEMS; PROBABILITY DISTRIBUTIONS;

GENES;

INSULIN; TRANSCRIPTION FACTOR;

ARTICLE; BAYES THEOREM; CONTROLLED STUDY; EVOLUTIONARY ALGORITHM; GENE EXPRESSION; GENE REGULATORY NETWORK; GENETIC ALGORITHM; GENETIC ENGINEERING; GLUCOSE HOMEOSTASIS; INTERMETHOD COMPARISON; LEARNING; LEARNING ALGORITHM; MUTATION; SIMULATION; TRANSCRIPTION REGULATION; BIOLOGICAL MODEL; COMPUTER SIMULATION; EVOLUTION; GENE EXPRESSION REGULATION; GENETIC VARIABILITY; GENETICS; MOLECULAR EVOLUTION; SIGNAL TRANSDUCTION;

COMPUTER SIMULATION; EVOLUTION; EVOLUTION, MOLECULAR; GENE EXPRESSION REGULATION; MODELS, GENETIC; SIGNAL TRANSDUCTION; TRANSCRIPTION FACTORS; VARIATION (GENETICS);

EID: 41549159731     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-9-91     Document Type: Article

References (48)

1. de Jong H. Modeling and simulation of genetic regulatory systems: A literature review Journal of Computational Biology 2002, 9(1):67-103. 10.1089/10665270252833208 11911796
2. van Someren EP Wessels LFA Backer E Reinders MJT Genetic network modeling Pharmacogenomics 2002, 3(4):507-525. 10.1517/14622416.3.4.507 12164774
3. Friedman N Linial M Nachman I Peer D Using Bayesian Networks to Analyze Expression Data J Comp Bio 2000, 7(3-4):601-620. 10.1089/ 106652700750050961 11108481
4. Segal E Shapira M Regev A Pe'er D Botstein D Koller D Friedman N Module networks: Identifying regulatory modules and their condition-specific regulators from gene expression data Nat Genet 2003, 34(2):166-76. 12740579
5. Pearl J Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference San Francisco, CA, USA: Morgan Kaufmann Publishers Inc 1988
6. Cowell RG Lauritzen SL David AP Spiegelhalter DJ Spiegelhater DJ Probabilistic Networks and Expert Systems Secaucus, NJ, USA: Springer-Verlag New York, Inc 1999
7. Jensen FV Bayesian Networks and Decision Graphs Secaucus, NJ, USA: Springer-Verlag New York, Inc 2001
8. Spirtes P Glymour C Scheines R Kauffman S Aimale V Wimberly F Constructing Bayesian Network Models of Gene Expression Networks from Microarray Data Proc. of the Atlantic Symposium on Computational Biology, Genome Information Systems & Technology 2000
9. Goto M Imoto Estimation of genetic networks and functional structures between genes by using Bayesian network and nonparametric regression Pacific Symposium on Biocomputin 2002, 7:175-186. http://bonsai.ims.u-tokyo.ac.jp/~imoto/imoto_psb2002.pdf 11928473
10. Pe'er D Regev A Elidan G Friedman N Inferring subnetworks from perturbed expression profiles Bioinformatics 2001, 17(Suppl 1):S215-S224. 11473012
11. Hartemink AJ Gifford DK Jaakkola TS Young RA Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks Pac Symp Biocomput 2001, 422-433. http://www.psrg.csail.mit.edu/pubs/psbcamera.pdf 11262961
12. Husmeier D Reverse engineering of genetic networks with Bayesian networks Biochemical Society Transactions 2003, 31:1516-1518. 14641102
13. Pena JM Bjorkegren J Tegner J Growing Bayesian network models of gene networks from seed genes Bioinformatics 2005, 21(Suppl 2):iI224-ii229. 10.1093/bioinformatics/bti1137 16204109
14. Buntine W A Guide to the Literature on Learning Probabilistic Networks from Data IEEE Transactions on Knowledge and Data Engineering 1996, 8(2):195-210. 10.1109/69.494161
15. Robinson R Counting unlabeled acyclic digraphs Lecture Notes in Mathematics Springer-Verlag 1977, 622
16. Chickering Optimal Structure identification with greedy search Journal of machine learning research 2002, 3:507-554. http://www.ai.mit.edu/ projects/jmlr/papers/volume3/chickering02b/source/chickering02b.pdf 10.1162/153244303321897717
17. Cooper GF Herskovits E A Bayesian Method for the Induction of Probabilistic Networks from Data Mach Learn 1992, 9(4):309-347
18. DM Chickering DG Heckermann D Learning bayesian networks is NP-complete Learning from data: AI and Statistics New York NY: Springer-Verlag Fisher D, Lenz HJ 1996, 5:121-130
19. Friedman Koller Being Bayesian About Network Structure: A Bayesian Approach to Structure Discovery in Bayesian Networks Machine Learning 2003, 50:95-126 10.1023/A:1020249912095
20. Kocka T Castelo R Improved learning of Bayesian networks UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence San Francisco, CA, USA: Morgan Kaufmann Publishers Inc 2001, 269-276
21. Wong ML Lam W Leung KS Using Evolutionary Programming and Minimum Description Length Principle for Data Mining of Bayesian Networks IEEE Transactions on Pattern Analysis and Machine Intelligence 1999, 21(2):174-178. 10.1109/34.748825
22. Le PP Bahl A Ungar LH Using prior knowledge to improve genetic network reconstruction from microarray data Silico Biology 2004, 4(3):335-53. 15724284
23. Friedman N Inferring Cellular Networks Using Probabilistic Graphical Models Science 2004, 303(5659):799-805. 10.1126/science.1094068 14764868
24. Bayes Net Toolbox http://www.cs.ubc.ca/~murphyk/Software/BNT/bnt.html
25. Holland JH Adaptation in Natural and Artificial Systems University of Michigan Press 1975
26. Goldberg DE Genetic Algorithms in Search, Optimization and Machine Learning Addison-Wesley Publishing Company, Inc 1989
27. Hsu WH Guo H Perry BB Stilson JA A Permutation Genetic Algorithm For Variable Ordering In Learning Bayesian Networks From Data GECCO '02: Proceedings of the Genetic and Evolutionary Computation Conference San Francisco, CA, USA: Morgan Kaufmann Publishers Inc 2002, 383-390
28. Larranaga P Kuijpers C Murga R Yurramendi Y Learning Bayesian network structures by searching for the best ordering with genetic algorithms 1996
29. Larranaga P Poza M Yurramendi Y Murga RH Kuijpers CMH Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters IEEE Trans Pattern Anal Mach Intell 1996, 18(9):912-926. 10.1109/34.537345
30. Etxeberria R Larranaga P Picaza JM Analysis of the behaviour of genetic algorithms when learning Bayesian network structure from data Pattern Recogn Lett 1997, 18(11-13):1269-1273. 10.1016/S0167-8655(97)00106-2
31. Myers JW Laskey KB DeJong KA Learning Bayesian Networks from Incomplete Data using Evolutionary Algorithms 1999, 1:458-465
32. Cotta C Muruzabal J Towards a More Efficient Evolutionary Induction of Bayesian Networks PPSN VII: Proceedings of the 7th International Conference on Parallel Problem Solving from Nature London, UK: Springer-Verlag 2002, 730-739
33. Cotta C Troya JM Analyzing Directed Acyclic Graph Recombination Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications London, UK: Springer-Verlag 2001, 739-748
34. Mahfoud SW Niching methods for genetic algorithms PhD thesis Champaign, IL, USA 1995
35. Jong KAD An analysis of the behavior of a class of genetic adaptive systems PhD thesis 1975
36. Chickering D A Transformational Characterization of Equivalent Bayesian Network Structures Proceedings of the 11th Annual Conference on Uncertainty in Artificial Intelligence (UAI-95) San Francisco, CA: Morgan Kaufmann 1995, 87-98
37. Verma T Pearl J Equivalence and Synthesis of Causal Models New York, NY: Elsevier Science 1991
38. Schölkopf B Smola A Müller K Kernel principal component analysis 7th International Conference on Artificial Neural Networks, ICANN 97, Lausanne, Switzerland Berlin: Springer Lecture Notes in Computer Science W Gerstner MH A Germond, Nicoud JD 1997, 1327:583-588
39. Gärtner T Driessens K Ramon J Graph Kernels and Gaussian Processes for Relational Reinforcement Learning Thirteenth International Conference on Inductive Logic Programming (ILP-2003) Springer 2003
40. SVM and Kernel Methods Matlab Toolbox http://asi.insa-rouen.fr/ enseignants/~arakotom/toolbox/index.html
41. Cheng J Greiner R Kelly J Bell D Liu W Learning Bayesian networks from data: An information-theory based approach Artif Intell 2002, 137(1-2):43-90. 10.1016/S0004-3702(02)00191-1
42. Tsamardinos I Brown LE Aliferis CF The max-min hill-climbing Bayesian network structure learning algorithm Machine Learning 2006, 65:31-78. 10.1007/s10994-006-6889-7
43. Peter Spirtes CG Scheines R Causation, Prediction, and Search The MIT Press 2000
44. Heckerman D Geiger D Likelihoods and parameter priors for Bayesian networks 1995
45. BNT Structure Learning Package http://bnt.insa-rouen.fr/ajouts.html
46. Aliferis CF Tsamardinos I Statnikov AR Brown LE Causal Explorer: A Causal Probabilistic Network Learning Toolkit for Biomedical Discovery 2003, 371-376
47. Quach M Brunel N d'Alché Buc F Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference. Bioinformatics 2007, 23:3209-3216. 18042557 10.1093/ bioinformatics/btm510
48. Giudici P Castelo R Improving Markov Chain Monte Carlo model search for Data Mining Machine Learning 2003, 50(1/2):127-158. 10.1023/ A:1020202028934