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Volumn 6, Issue 12, 2011, Pages

Gene regulatory network reconstruction using bayesian networks, the dantzig selector, the lasso and their meta-analysis

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTIC METHOD; ARTICLE; BAYES THEOREM; DANTZIG SELECTOR; DIAGNOSTIC ACCURACY; GENE ACTIVITY; GENE MUTATION; GENE REGULATORY NETWORK; GENETIC ANALYSIS; GENETIC VARIABILITY; GOLD STANDARD; LINEAR REGRESSION ANALYSIS; MATHEMATICAL COMPUTING; RECEIVER OPERATING CHARACTERISTIC; RELIABILITY; SCORING SYSTEM; STATISTICAL MODEL; STRUCTURE ANALYSIS; GENOMICS; META ANALYSIS; MUTATION;

EID: 84455173311     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0029165     Document Type: Article
Times cited : (87)

References (63)
  • 2
    • 0042856391 scopus 로고    scopus 로고
    • Trans-acting regulatory variation in saccharomyces cerevisiae and the role of transcription factors
    • Yvert G, Brem R, Whittle J, Akey J, Foss E, et al. (2003) Trans-acting regulatory variation in saccharomyces cerevisiae and the role of transcription factors. Nature genetics 35: 57-64.
    • (2003) Nature Genetics , vol.35 , pp. 57-64
    • Yvert, G.1    Brem, R.2    Whittle, J.3    Akey, J.4    Foss, E.5
  • 3
    • 49449086368 scopus 로고    scopus 로고
    • Survival of the sparsest: robust gene networks are parsimonious
    • Leclerc R, (2008) Survival of the sparsest: robust gene networks are parsimonious. Molecular Systems Biology 4.
    • (2008) Molecular Systems Biology , vol.4
    • Leclerc, R.1
  • 5
    • 33845637127 scopus 로고    scopus 로고
    • Functional and evolutionary inference in gene networks: does topology matter?
    • Siegal M, Promislow D, Bergman A, (2007) Functional and evolutionary inference in gene networks: does topology matter? Genetica 129: 83-103.
    • (2007) Genetica , vol.129 , pp. 83-103
    • Siegal, M.1    Promislow, D.2    Bergman, A.3
  • 7
    • 0014671878 scopus 로고
    • Homeostasis and differentiation in random genetic control networks
    • Kauffman S, (1969) Homeostasis and differentiation in random genetic control networks. Nature 224: 177-178.
    • (1969) Nature , vol.224 , pp. 177-178
    • Kauffman, S.1
  • 8
    • 0015823097 scopus 로고
    • Boolean formalization of genetic control circuits
    • Thomas R, (1973) Boolean formalization of genetic control circuits. Journal of Theoretical Biology 42: 563-585.
    • (1973) Journal of Theoretical Biology , vol.42 , pp. 563-585
    • Thomas, R.1
  • 9
    • 0742305866 scopus 로고    scopus 로고
    • Network biology: understanding the cells functional organization
    • Barabási AL, Oltvai Z, (2004) Network biology: understanding the cells functional organization. Nature Reviews Genetics 5: 101-113.
    • (2004) Nature Reviews Genetics , vol.5 , pp. 101-113
    • Barabási, A.L.1    Oltvai, Z.2
  • 10
    • 0033736476 scopus 로고    scopus 로고
    • Genetic network inference: from co-expression clustering to reverse engineering
    • Bioinformatics
    • Dhaeseleer1 P, Liang S, Somogyi R, (2000) Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics.
    • (2000)
    • Dhaeseleer1, P.1    Liang, S.2    Somogyi, R.3
  • 11
    • 0038048325 scopus 로고    scopus 로고
    • Inferring genetic networks and identifying compound mode of action via expression profiling
    • Gardner T, di Bernardo D, Lorenz D, Collins J, (2003) Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301: 102-105.
    • (2003) Science , vol.301 , pp. 102-105
    • Gardner, T.1    di Bernardo, D.2    Lorenz, D.3    Collins, J.4
  • 12
    • 1642403354 scopus 로고    scopus 로고
    • Identification of genetic networks
    • Xiong M, Li J, Fang X, (2004) Identification of genetic networks. Genetics 166: 1037-1062.
    • (2004) Genetics , vol.166 , pp. 1037-1062
    • Xiong, M.1    Li, J.2    Fang, X.3
  • 14
    • 34548388925 scopus 로고    scopus 로고
    • Inference of gene networks from temporal gene expression profiles
    • Bansal M, di Bernardo D, (2007) Inference of gene networks from temporal gene expression profiles. IET Systems Biology 1: 306-312.
    • (2007) IET Systems Biology , vol.1 , pp. 306-312
    • Bansal, M.1    di Bernardo, D.2
  • 17
    • 46049101810 scopus 로고    scopus 로고
    • Gene regulatory network reconstruction by Bayesian integration of prior knowledge and/or different experimental conditions
    • Werhli A, Husmeier D, (2008) Gene regulatory network reconstruction by Bayesian integration of prior knowledge and/or different experimental conditions. Journal of Bioinformatics and Computational Biology 6: 543-572.
    • (2008) Journal of Bioinformatics and Computational Biology , vol.6 , pp. 543-572
    • Werhli, A.1    Husmeier, D.2
  • 18
    • 84855854603 scopus 로고    scopus 로고
    • D5c3 - Dream initiative. Available:. Organizers: Columbia university and IBM
    • de la Fuente A, Stolovitzky G, (2010) D5c3- Dream initiative. Available: http://wiki.c2b2.columbia.edu/dream/index.php/D5c3. Organizers: Columbia university and IBM.
    • (2010)
    • de la Fuente, A.1    Stolovitzky, G.2
  • 19
    • 0004245434 scopus 로고
    • The Design of Experiments
    • Edinburgh, London, Oliver and Boyd
    • Fisher R, (1935) The Design of Experiments. Edinburgh, London Oliver and Boyd.
    • (1935)
    • Fisher, R.1
  • 20
    • 0035400051 scopus 로고    scopus 로고
    • Genetical genomics: the added value from segregation
    • Jansen R, Nap N, (2001) Genetical genomics: the added value from segregation. Trends in Genetics 17: 388-391.
    • (2001) Trends in Genetics , vol.17 , pp. 388-391
    • Jansen, R.1    Nap, N.2
  • 21
    • 0037306536 scopus 로고    scopus 로고
    • Studying complex biological systems using multifactorial perturbation
    • Jansen R, (2003) Studying complex biological systems using multifactorial perturbation. Nature Reviews in Genetics 4: 145-151.
    • (2003) Nature Reviews in Genetics , vol.4 , pp. 145-151
    • Jansen, R.1
  • 22
    • 44149084690 scopus 로고    scopus 로고
    • Using genetic markers to orient the edges in quantitative trait networks: the NEO software
    • Aten J, Fuller T, Lusis A, Horvath S, (2008) Using genetic markers to orient the edges in quantitative trait networks: the NEO software. BMC Bioinformatics 2.
    • (2008) BMC Bioinformatics , vol.2
    • Aten, J.1    Fuller, T.2    Lusis, A.3    Horvath, S.4
  • 23
    • 34247556038 scopus 로고    scopus 로고
    • Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations
    • Zhu J, Wiener M, Zhang C, Fridman A, Minch E, et al. (2007) Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations. PLoS Computational Biology 3: e69.
    • (2007) PLoS Computational Biology , vol.3
    • Zhu, J.1    Wiener, M.2    Zhang, C.3    Fridman, A.4    Minch, E.5
  • 24
    • 45149096374 scopus 로고    scopus 로고
    • Gene network inference via structural equation modeling in genetical genomics experiments
    • Liu B, de la Fuente A, Hoeschele I, (2008) Gene network inference via structural equation modeling in genetical genomics experiments. Genetics 178: 1763-1776.
    • (2008) Genetics , vol.178 , pp. 1763-1776
    • Liu, B.1    de la Fuente, A.2    Hoeschele, I.3
  • 26
    • 34548275795 scopus 로고    scopus 로고
    • The Dantzig selector: Statistical estimation when p is much larger than n
    • Candès E, Tao T, (2007) The Dantzig selector: Statistical estimation when p is much larger than n. Annals of Statistics 35: 2313-2351.
    • (2007) Annals of Statistics , vol.35 , pp. 2313-2351
    • Candès, E.1    Tao, T.2
  • 27
    • 63849210631 scopus 로고    scopus 로고
    • Lessons from the DREAM2 challenges
    • In: Stolovitzky G, Kahlem P, Califano A, editors
    • Stolovitzky G, Prill R, Califano A, (2009) Lessons from the DREAM2 challenges. In: Stolovitzky G, Kahlem P, Califano A, editors. Annals of the New York Academy of Sciences volume 1158: 159-195.
    • (2009) Annals of the New York Academy of Sciences , vol.volume 1158 , pp. 159-195
    • Stolovitzky, G.1    Prill, R.2    Califano, A.3
  • 28
    • 33847268619 scopus 로고    scopus 로고
    • Robustness can evolve gradually in complex regulatory gene networks with varying topology
    • Ciliberti S, Martin O, Wagner A, (2007) Robustness can evolve gradually in complex regulatory gene networks with varying topology. PLoS Computational Biology 3: e15.
    • (2007) PLoS Computational Biology , vol.3
    • Ciliberti, S.1    Martin, O.2    Wagner, A.3
  • 31
    • 71149111418 scopus 로고    scopus 로고
    • Structure learning of Bayesian networks using constraints
    • de Campos C, Zeng Z, Ji Q, (2009) Structure learning of Bayesian networks using constraints. In: Proc. of ICML '09 pp. 113-120.
    • (2009) In: Proc. Of ICML '09 , pp. 113-120
    • de Campos, C.1    Zeng, Z.2    Ji, Q.3
  • 32
    • 77958539511 scopus 로고    scopus 로고
    • Properties of Bayesian Dirichlet scores to learn Bayesian network structures
    • In: Fox M, Poole D, editors, Atlanta, Georgia, USA, AAAI Press
    • de Campos C, Ji Q, (2010) Properties of Bayesian Dirichlet scores to learn Bayesian network structures. In: Fox M, Poole D, editors. Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence Atlanta, Georgia, USA AAAI Press pp. 431-436.
    • (2010) Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence , pp. 431-436
    • de Campos, C.1    Ji, Q.2
  • 35
    • 68649086910 scopus 로고    scopus 로고
    • Simultaneous analysis of lasso and Dantzig selector
    • Bickel P, Ritov Y, Tsybakov A, (2009) Simultaneous analysis of lasso and Dantzig selector. Annals of statistics 37: 1705-1732.
    • (2009) Annals of Statistics , vol.37 , pp. 1705-1732
    • Bickel, P.1    Ritov, Y.2    Tsybakov, A.3
  • 36
    • 77949644952 scopus 로고    scopus 로고
    • Towards a rigorous assessment of systems biology models: The DREAM3 challenges
    • Prill R, Marbach D, Saez-Rodriguez J, Sorger P, Alexopoulos L, et al. (2010) Towards a rigorous assessment of systems biology models: The DREAM3 challenges. PLOS ONE 5: e9202.
    • (2010) PLOS ONE , vol.5
    • Prill, R.1    Marbach, D.2    Saez-Rodriguez, J.3    Sorger, P.4    Alexopoulos, L.5
  • 38
    • 33645756373 scopus 로고    scopus 로고
    • Combined expression trait correlations and expression quantitative trait locus mapping
    • Chen HLM, Flowers J, Yandell B, Stapleton D, Mata C, et al. (2006) Combined expression trait correlations and expression quantitative trait locus mapping. PLoS Genetics 2: e6.
    • (2006) PLoS Genetics , vol.2
    • Chen, H.L.M.1    Flowers, J.2    Yandell, B.3    Stapleton, D.4    Mata, C.5
  • 39
    • 77956841911 scopus 로고    scopus 로고
    • A global analysis of qtls for expression variations in rice shoots at the early seedling stage
    • Wang J, Yu H, Xie W, Xing Y, Yu S, et al. (2010) A global analysis of qtls for expression variations in rice shoots at the early seedling stage. The Plant Journal 63: 1063-1074.
    • (2010) The Plant Journal , vol.63 , pp. 1063-1074
    • Wang, J.1    Yu, H.2    Xie, W.3    Xing, Y.4    Yu, S.5
  • 40
    • 33847768279 scopus 로고    scopus 로고
    • Impact of dense genetic marker maps on plant population genetic studies
    • Weir B, (2007) Impact of dense genetic marker maps on plant population genetic studies. Euphytica 154: 355-364.
    • (2007) Euphytica , vol.154 , pp. 355-364
    • Weir, B.1
  • 41
    • 73849097267 scopus 로고    scopus 로고
    • Information-theoretic limits on sparsity recovery in the high-dimensional and noisy setting
    • Wainwright M, (2009) Information-theoretic limits on sparsity recovery in the high-dimensional and noisy setting. IEEE Transactions on Information Theory 55: 5728-5741.
    • (2009) IEEE Transactions on Information Theory , vol.55 , pp. 5728-5741
    • Wainwright, M.1
  • 42
    • 0035470889 scopus 로고    scopus 로고
    • Greedy function approximation: a gradient boosting machine
    • Annals of Statistics
    • Friedman J, (2001) Greedy function approximation: a gradient boosting machine. Annals of Statistics.
    • (2001)
    • Friedman, J.1
  • 43
    • 56449120785 scopus 로고    scopus 로고
    • Bolasso: model consistent lasso estimation through the bootstrap
    • In: Cohen W, McCallum A, Roweis S, editors, Helsinki, Finland
    • Bach F, (2008) Bolasso: model consistent lasso estimation through the bootstrap. In: Cohen W, McCallum A, Roweis S, editors. Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML) volume 307 of ACM International Conference Proceeding Series Helsinki, Finland pp. 25-32.
    • (2008) Proceedings of the Twenty-Fifth International Conference on Machine Learning (ICML) , pp. 25-32
    • Bach, F.1
  • 44
    • 0003684449 scopus 로고    scopus 로고
    • The Elements of Statistical Learning: Data Mining, Inference, and Prediction
    • Series in Statistics. Springer, second edition
    • Hastie T, Tibshirani R, Friedman J, (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Series in Statistics. Springer, second edition.
    • (2009)
    • Hastie, T.1    Tibshirani, R.2    Friedman, J.3
  • 45
    • 34548183010 scopus 로고    scopus 로고
    • "ideal parent" structure learning for continuous variable Bayesian networks
    • Elidan G, Nachman I, Friedman N, (2007) "ideal parent" structure learning for continuous variable Bayesian networks. Journal of Machine Learning Research 8: 1799-1833.
    • (2007) Journal of Machine Learning Research , vol.8 , pp. 1799-1833
    • Elidan, G.1    Nachman, I.2    Friedman, N.3
  • 46
    • 85162054206 scopus 로고    scopus 로고
    • Probabilistic latent variable models for distinguishing between cause and effect
    • In: Lafferty J, Williams C, Shawe-Taylor J, Zemel R, Culotta A, editors
    • Mooij J, Stegle O, Janzing D, Zhang K, Schölkopf B, (2010) Probabilistic latent variable models for distinguishing between cause and effect. In: Lafferty J, Williams C, Shawe-Taylor J, Zemel R, Culotta A, editors. Advances in Neural Information Processing Systems 23: 1687-1695.
    • (2010) Advances in Neural Information Processing Systems , vol.23 , pp. 1687-1695
    • Mooij, J.1    Stegle, O.2    Janzing, D.3    Zhang, K.4    Schölkopf, B.5
  • 47
    • 4444292685 scopus 로고    scopus 로고
    • Kernel Methods in Computational Biology
    • MIT PRess
    • Schölkopf B, Tsuda K, Vert JP, eds. (2004) Kernel Methods in Computational Biology. MIT PRess.
    • (2004)
    • Schölkopf, B.1    Tsuda, K.2    Vert, J.P.3
  • 48
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • Breiman L, (2001) Random forests. Machine Learning 45: 5-32.
    • (2001) Machine Learning , vol.45 , pp. 5-32
    • Breiman, L.1
  • 50
    • 77958570788 scopus 로고    scopus 로고
    • Inferring regulatory networks from expression data using tree-based methods
    • Huynh-Thu V, Irrthum A, Wehenkel L, Geurts P, (2010) Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 5: e12776.
    • (2010) PLoS ONE , vol.5
    • Huynh-Thu, V.1    Irrthum, A.2    Wehenkel, L.3    Geurts, P.4
  • 52
    • 70350678796 scopus 로고    scopus 로고
    • On sensitivity of the MAP Bayesian network structure to the equivalent sample size parameter
    • Vancouver, Canada
    • Silander T, Kontkanen P, Myllym̈aki P, (2007) On sensitivity of the MAP Bayesian network structure to the equivalent sample size parameter. In: Proc. of UAI-07 Vancouver, Canada pp. 360-367.
    • (2007) In: Proc. Of UAI-07 , pp. 360-367
    • Silander, T.1    Kontkanen, P.2    Myllym̈aki, P.3
  • 53
    • 70349888140 scopus 로고    scopus 로고
    • Learning the Bayesian network structure: Dirichlet prior vs data
    • Steck H, (2008) Learning the Bayesian network structure: Dirichlet prior vs data. In: UAI pp. 511-518.
    • (2008) In: UAI , pp. 511-518
    • Steck, H.1
  • 54
    • 77957921133 scopus 로고    scopus 로고
    • Comparative study of discretization methods of microarray data for inferring transcriptional regulatory networks
    • Yong L, Lili L, Xi B, Hua C, Wei J, et al. (2010) Comparative study of discretization methods of microarray data for inferring transcriptional regulatory networks. BMC Bioinformatics 11: 520.
    • (2010) BMC Bioinformatics , vol.11 , pp. 520
    • Yong, L.1    Lili, L.2    Xi, B.3    Hua, C.4    Wei, J.5
  • 56
    • 22844441552 scopus 로고    scopus 로고
    • Reverse engineering gene regulatory networks
    • Hartemink A, (2005) Reverse engineering gene regulatory networks. Nature Biotechnology 23: 554-555.
    • (2005) Nature Biotechnology , vol.23 , pp. 554-555
    • Hartemink, A.1
  • 61
    • 51249181779 scopus 로고
    • A new polynomial-time algorithm for linear programming
    • Karmarkar N, (1984) A new polynomial-time algorithm for linear programming. Combinatorica 4: 373-395.
    • (1984) Combinatorica , vol.4 , pp. 373-395
    • Karmarkar, N.1
  • 62
    • 77956908268 scopus 로고    scopus 로고
    • A path following algorithm for sparse pseudo-likelihood inverse covariance estimation (SPLICE)
    • Technical Report 769, Statistics Department, UC Berkeley
    • Rocha G, Zhao P, Yu B, (2008) A path following algorithm for sparse pseudo-likelihood inverse covariance estimation (SPLICE). Technical Report 769, Statistics Department, UC Berkeley.
    • (2008)
    • Rocha, G.1    Zhao, P.2    Yu, B.3
  • 63
    • 0003583491 scopus 로고
    • Statistical methods for meta-analysis
    • Academic Press
    • Hedges L, Olkin I, (1985) Statistical methods for meta-analysis. Academic Press.
    • (1985)
    • Hedges, L.1    Olkin, I.2


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