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A posterior probability approach for gene regulatory network inference in genetic perturbation data
1. | University of Washington, Department of Statistics, Box 354322, Seattle, WA 98195-4322, United States, United States |
2. | University of Washington, Institute of Technology, Box 358426, 1900 Commerce Street, Tacoma, WA 98402-3100, United States |
References:
[1] |
M. Bansal, G. Della Gatta and D. Di Bernardo, Inference of gene regulatory networks and compound mode of action from time course gene expression profiles, Bioinformatics, 22 (2006), 815-822.
doi: 10.1093/bioinformatics/btl003. |
[2] |
K. Basso, A. A. Margolin, G. Stolovitzky, U. Klein, R. D. Favera and A. Califano, Reverse engineering of regulatory networks in human B cells, Nature Genetics, 37 (2005), 382-390.
doi: 10.1038/ng1532. |
[3] |
P. Bühlmann, M. Kalisch and L. Meier, High-dimensional statistics with a view towards applications in biology, Annual Review of Statistics and Its Application, 1 (2014), 255-278. |
[4] |
E. Y. Chen, C. M. Tan, Y. Kou, Q. Duan, Z. Wang, G. V. Meirelles, N. R. Clark and A. Ma'ayan, Enrichr: Interactive and collaborative HTML5 gene list enrichment analysis tool, BMC Bioinformatics, 14 (2013), 128-141.
doi: 10.1186/1471-2105-14-128. |
[5] |
S. Christley, Q. Nie and X. Xie, Incorporating existing network information into gene network inference, PLoS One, 4 (2009), e6799.
doi: 10.1371/journal.pone.0006799. |
[6] |
M. Clyde and E. I. George, Model uncertainty, Statistical Science, 19 (2004), 81-94.
doi: 10.1214/088342304000000035. |
[7] |
A. P. Dempster, N. M. Laird and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society. Series B (Methodological), 39 (1977), 1-38. |
[8] |
P. D'haeseleer, X. Wen, S. Fuhrman and R. Somogyi, Linear modeling of mRNA expression levels during CNS development and injury, Pacific Symposium on Biocomputing, 4 (1999), 41-52.
doi: 10.1142/9789814447300_0005. |
[9] |
C. Ding and H. Peng, Minimum redundancy feature selection from microarray gene expression data, Bioinformatics Conference, 2003. CSB 2003. Proceedings of the 2003 IEEE , (2003), 523-528.
doi: 10.1109/CSB.2003.1227396. |
[10] |
, DREAM4 In Silico Network Challenge, website,, , ().
|
[11] |
Q. Duan, C. Flynn, M. Niepel, M. Hafner, J. M. Muhlich, N. F. Fernandez, A. D. Rouillard, C. M. Tan, E. Y. Chen, T. R. Golub, P. K. Sorger, A. Subramanian and A. Ma'ayan, LINCS Canvas Browser: Interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures, Nucleic Acids Research, 42 (2014), w449-w460.
doi: 10.1093/nar/gku476. |
[12] |
S. A. Dunbar, Applications of Luminex® $xMAP^{TM}$ technology for rapid, high-throughput multiplexed nucleic acid detection, Clinica Chimica Acta, 363 (2006), 71-82. |
[13] |
J. J. Faith, B. Hayete, J. T. Thaden, I. Mogno, J. Wierzbowski, G. Cottarel, S. Kasif, J. J. Collins and T. S. Gardner, Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles, PLoS Biol, 5 (2007), e8.
doi: 10.1371/journal.pbio.0050008. |
[14] |
N. Friedman, M. Linial, I. Nahman and D. Pe'er, Using bayesian networks to analyze expression data, RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology, (2000), 127-135.
doi: 10.1145/332306.332355. |
[15] |
H. Fröhlich, M. Fellmann, H. Sueltmann, A. Poustka and T.Beissbarth, Large scale statistical inference of signaling pathways from RNAi and microarray data, BMC Bioinformatics, 8 (2007), 386. |
[16] |
N. Guelzim, S. Bottani, P. Bourgine and F. Kèpés, Topological and causal structure of the yeast transcriptional regulatory network, Nature Genetics, 31 (2002), 60-63.
doi: 10.1038/ng873. |
[17] |
M. Gustafsson, M. Hörnquist, J. Lundström, J. Björkegren and J. Tegnér, Reverse engineering of gene networks with LASSO and nonlinear basis functions, Annals of the New York Academy of Sciences, 1158 (2009), 265-275.
doi: 10.1111/j.1749-6632.2008.03764.x. |
[18] |
M. Hecker, S. Lambeck, S. Toepfer, E. Someren and R. Guthke, Gene regulatory network inference: Data integration in dynamic models, A review, Biosystems, 96 (2009), 86-103.
doi: 10.1016/j.biosystems.2008.12.004. |
[19] |
J. A. Hoeting, D. Madigan, A. E. Raftery and C. T. Volinsky, Bayesian model averaging: A tutorial, Statistical Science, 14 (1999), 382-417.
doi: 10.1214/ss/1009212519. |
[20] |
R. E. Kass and A. E. Raftery, Bayes factors, Journal of the American Statistical Association, 90 (1995), 773-795.
doi: 10.1080/01621459.1995.10476572. |
[21] |
S. Y. Kim, S. Imoto and S. Miyano, Inferring gene networks from time series microarray data using dynamic Bayesian networks, Briefings in Bioinformatics, 4 (2003), 228-235.
doi: 10.1093/bib/4.3.228. |
[22] |
S. Y. Kim, S. Imoto and S. Miyano, Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data, Computational Methods in Systems Biology, 2602 (2003), 104-113.
doi: 10.1007/3-540-36481-1_9. |
[23] |
S. Klamt, R. J. Flassig and K. Sundmacher, TRANSWESD: inferring cellular networks with transitive reduction, Bioinformatics, 26 (2010), 2160-2168.
doi: 10.1093/bioinformatics/btq342. |
[24] |
S. Lèbre, J. Becq, F. Devaus, M. Stumpf and G. Lelandais, Statistical inference of the time-varying structure of gene-regulation networks, BMC Systems Biology, 4 (2010), article 130. |
[25] |
W. Lee and W. Tzou, Computational methods for discovering gene networks from expression data, Briefings in Bioinformatics, 10 (2009), 408-423.
doi: 10.1093/bib/bbp028. |
[26] |
J. Li and R. Tibshirani, Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data, Statistical Methods in Medical Research, 22 (2013), 519-536.
doi: 10.1177/0962280211428386. |
[27] |
Z. Li, P. Li, A. Krishnan and J. Liu, Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis, Bioinformatics, 27 (2011), 2686-2691.
doi: 10.1093/bioinformatics/btr454. |
[28] |
, Library of Integrated Network-based Cellular Signatures (LINCS), website,, , ().
|
[29] |
K. Lo, A. E. Raftery, K. M. Dombeck, J. Zhu, E. E. Schadt, R. E. Bumgarner and K. Y. Yeung, Integrating external biological knowledge in the construction of regulatory networks from time-series expression data, BMC Systems Biology, 6 (2012), p101.
doi: 10.1186/1752-0509-6-101. |
[30] |
F. M. Lopes, E. A. de Oliveira and R. M. Cesar, Inference of gene regulatory networks from time series by Tsallis entropy, BMC Systems Biology, 5 (2011), p61.
doi: 10.1186/1752-0509-5-61. |
[31] |
M. J. McGeachie, H. Chang and S. T. Weiss, CGBayesNets: Conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data, PLoS Computational Biology, 10 (2014), e1003676.
doi: 10.1371/journal.pcbi.1003676. |
[32] |
D. Marbach, T. Schaffter, C. Mattiussi and D. Floreano, Generating realistic in silico gene networks for performance assessment of reverse engineering methods, Journal of Computational Biology, 16 (2009), 229-239. |
[33] |
D. Marbach, R. J. Prill, T. Schaffter, C. Mattiussi, D. Floreano and G. Stolovitzky, Revealing strengths and weaknesses of methods for gene network inference, Proceedings of the National Academy of Sciences, 107 (2010), 6286-6291.
doi: 10.1073/pnas.0913357107. |
[34] |
A. A. Margolin, I. Nemenman, K. Basso, C. Wiggins, G. Stolovitzky, R. D. Favera and A. Califano, ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context, BMC Bioinformatics, 7 (2006), S7.
doi: 10.1186/1471-2105-7-S1-S7. |
[35] |
F. Markowetz and R. Spang, Inferring cellular networks: A review, BMC Bioinformatics, 8 (2007), S5.
doi: 10.1186/1471-2105-8-S6-S5. |
[36] |
P. Menéndez, Y. Kourmpetis, C. J. ter Braak and F. A. van Eeuwijk, Gene regulatory networks from multifactorial perturbations using Graphical Lasso: Application to the DREAM4 challenge, PLoS One, 5 (2010), e14147. |
[37] |
P. E. Meyer, K. Kontos, F. Lafitte and G. Bontempi, Information-theoretic inference of large transcriptional regulatory networks, EURASIP Journal on Bioinformatics and Systems Biology, 2007 (2007), 79879.
doi: 10.1155/2007/79879. |
[38] |
P. E. Meyer, F. Lafitte and G. Bontempi, minet: A R/Bioconductor package for inferring large transcriptional networks using mutual information, BMC Bioinformatics, 9 (2008), article 461.
doi: 10.1186/1471-2105-9-461. |
[39] |
G. Michailidis and F. d'Alché-Buc, Autoregressive models for gene regulatory network inference: Sparsity, stability and causality issues, Mathematical Biosciences, 246 (2013), 326-334.
doi: 10.1016/j.mbs.2013.10.003. |
[40] |
K. Murphy and S. Mian, Modelling Gene Expression Data Using Dynamic Bayesian Networks, Vol. 104. Technical report, Computer Science Division, University of California, Berkeley, CA, 1999. |
[41] |
A. Pinna, N. Soranzo and A. De La Fuente, From knockouts to networks: Establishing direct cause-effect relationships through graph analysis, PLoS One, 5 (2010), e12912.
doi: 10.1371/journal.pone.0012912. |
[42] |
A. E. Raftery, D. Madigan and J. A. Hoeting, Bayesian model averaging for linear regression models, Journal of the American Statistical Association, 92 (1997), 179-191.
doi: 10.1080/01621459.1997.10473615. |
[43] |
A. E. Raftery, Bayes factors and BIC, Sociological Methods & Research, 27 (1999), 411-417. |
[44] |
S. Rogers and M. Girolami, A Bayesian regression approach to the inference of regulatory networks from gene expression data, Bioinformatics, 21 (2005), 3131-3137.
doi: 10.1093/bioinformatics/bti487. |
[45] |
F. H. M. Salleh, M. A. Arif, S. Zainudin and M. Firdaus-Raih, Reconstructing gene regulatory networks from knockout data using Gaussian Noise Model and Pearson Correlation Coefficient, Computational Biology and Chemistry, 59 (2015), 3-14. |
[46] |
M. Sanchez-Castillo, I. Tienda-Luna, D. Blanco, M. C. Carrion-Perez and Y. Huang, Bayesian sparse factor model for transcriptional regulatory networks inference, Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European, (2013), 1-4. |
[47] |
A. Sandelin, W. Alkema, P. Engström, W. W. Wasserman and B. Lenhard, JASPAR: an open-access database for eukaryotic transcription factor binding profiles, Nucleic Acids Research, 32 (2004), D91-D94. |
[48] |
M. Scutari, Learning Bayesian Networks with the bnlearn R Package, Journal of Statistical Software, 35 (2010), 1-22. |
[49] |
A. Shojaie and G. Michailidis, Analysis of gene sets based on the underlying regulatory network, Journal of Computational Biology, 16 (2009), 407-426.
doi: 10.1089/cmb.2008.0081. |
[50] |
A. Shojaie and G. Michailidis, Discovering graphical Granger causality using the truncating lasso penalty, Bioinformatics, 26 (2010), i517-i523.
doi: 10.1093/bioinformatics/btq377. |
[51] |
A. Shojaie, A. Jauhiainen, M. Kallitsis and G. Michailidis, Inferring regulatory networks by combining perturbation screens and steady state gene expression profiles, PLoS One, 9 (2014), e82393.
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W. C. Young, A. E. Raftery and K. Y. Yeung, Fast Bayesian inference for gene regulatory networks using ScanBMA, BMC Systems Biology, 8 (2014), 47-57.
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show all references
References:
[1] |
M. Bansal, G. Della Gatta and D. Di Bernardo, Inference of gene regulatory networks and compound mode of action from time course gene expression profiles, Bioinformatics, 22 (2006), 815-822.
doi: 10.1093/bioinformatics/btl003. |
[2] |
K. Basso, A. A. Margolin, G. Stolovitzky, U. Klein, R. D. Favera and A. Califano, Reverse engineering of regulatory networks in human B cells, Nature Genetics, 37 (2005), 382-390.
doi: 10.1038/ng1532. |
[3] |
P. Bühlmann, M. Kalisch and L. Meier, High-dimensional statistics with a view towards applications in biology, Annual Review of Statistics and Its Application, 1 (2014), 255-278. |
[4] |
E. Y. Chen, C. M. Tan, Y. Kou, Q. Duan, Z. Wang, G. V. Meirelles, N. R. Clark and A. Ma'ayan, Enrichr: Interactive and collaborative HTML5 gene list enrichment analysis tool, BMC Bioinformatics, 14 (2013), 128-141.
doi: 10.1186/1471-2105-14-128. |
[5] |
S. Christley, Q. Nie and X. Xie, Incorporating existing network information into gene network inference, PLoS One, 4 (2009), e6799.
doi: 10.1371/journal.pone.0006799. |
[6] |
M. Clyde and E. I. George, Model uncertainty, Statistical Science, 19 (2004), 81-94.
doi: 10.1214/088342304000000035. |
[7] |
A. P. Dempster, N. M. Laird and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society. Series B (Methodological), 39 (1977), 1-38. |
[8] |
P. D'haeseleer, X. Wen, S. Fuhrman and R. Somogyi, Linear modeling of mRNA expression levels during CNS development and injury, Pacific Symposium on Biocomputing, 4 (1999), 41-52.
doi: 10.1142/9789814447300_0005. |
[9] |
C. Ding and H. Peng, Minimum redundancy feature selection from microarray gene expression data, Bioinformatics Conference, 2003. CSB 2003. Proceedings of the 2003 IEEE , (2003), 523-528.
doi: 10.1109/CSB.2003.1227396. |
[10] |
, DREAM4 In Silico Network Challenge, website,, , ().
|
[11] |
Q. Duan, C. Flynn, M. Niepel, M. Hafner, J. M. Muhlich, N. F. Fernandez, A. D. Rouillard, C. M. Tan, E. Y. Chen, T. R. Golub, P. K. Sorger, A. Subramanian and A. Ma'ayan, LINCS Canvas Browser: Interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures, Nucleic Acids Research, 42 (2014), w449-w460.
doi: 10.1093/nar/gku476. |
[12] |
S. A. Dunbar, Applications of Luminex® $xMAP^{TM}$ technology for rapid, high-throughput multiplexed nucleic acid detection, Clinica Chimica Acta, 363 (2006), 71-82. |
[13] |
J. J. Faith, B. Hayete, J. T. Thaden, I. Mogno, J. Wierzbowski, G. Cottarel, S. Kasif, J. J. Collins and T. S. Gardner, Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles, PLoS Biol, 5 (2007), e8.
doi: 10.1371/journal.pbio.0050008. |
[14] |
N. Friedman, M. Linial, I. Nahman and D. Pe'er, Using bayesian networks to analyze expression data, RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology, (2000), 127-135.
doi: 10.1145/332306.332355. |
[15] |
H. Fröhlich, M. Fellmann, H. Sueltmann, A. Poustka and T.Beissbarth, Large scale statistical inference of signaling pathways from RNAi and microarray data, BMC Bioinformatics, 8 (2007), 386. |
[16] |
N. Guelzim, S. Bottani, P. Bourgine and F. Kèpés, Topological and causal structure of the yeast transcriptional regulatory network, Nature Genetics, 31 (2002), 60-63.
doi: 10.1038/ng873. |
[17] |
M. Gustafsson, M. Hörnquist, J. Lundström, J. Björkegren and J. Tegnér, Reverse engineering of gene networks with LASSO and nonlinear basis functions, Annals of the New York Academy of Sciences, 1158 (2009), 265-275.
doi: 10.1111/j.1749-6632.2008.03764.x. |
[18] |
M. Hecker, S. Lambeck, S. Toepfer, E. Someren and R. Guthke, Gene regulatory network inference: Data integration in dynamic models, A review, Biosystems, 96 (2009), 86-103.
doi: 10.1016/j.biosystems.2008.12.004. |
[19] |
J. A. Hoeting, D. Madigan, A. E. Raftery and C. T. Volinsky, Bayesian model averaging: A tutorial, Statistical Science, 14 (1999), 382-417.
doi: 10.1214/ss/1009212519. |
[20] |
R. E. Kass and A. E. Raftery, Bayes factors, Journal of the American Statistical Association, 90 (1995), 773-795.
doi: 10.1080/01621459.1995.10476572. |
[21] |
S. Y. Kim, S. Imoto and S. Miyano, Inferring gene networks from time series microarray data using dynamic Bayesian networks, Briefings in Bioinformatics, 4 (2003), 228-235.
doi: 10.1093/bib/4.3.228. |
[22] |
S. Y. Kim, S. Imoto and S. Miyano, Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data, Computational Methods in Systems Biology, 2602 (2003), 104-113.
doi: 10.1007/3-540-36481-1_9. |
[23] |
S. Klamt, R. J. Flassig and K. Sundmacher, TRANSWESD: inferring cellular networks with transitive reduction, Bioinformatics, 26 (2010), 2160-2168.
doi: 10.1093/bioinformatics/btq342. |
[24] |
S. Lèbre, J. Becq, F. Devaus, M. Stumpf and G. Lelandais, Statistical inference of the time-varying structure of gene-regulation networks, BMC Systems Biology, 4 (2010), article 130. |
[25] |
W. Lee and W. Tzou, Computational methods for discovering gene networks from expression data, Briefings in Bioinformatics, 10 (2009), 408-423.
doi: 10.1093/bib/bbp028. |
[26] |
J. Li and R. Tibshirani, Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data, Statistical Methods in Medical Research, 22 (2013), 519-536.
doi: 10.1177/0962280211428386. |
[27] |
Z. Li, P. Li, A. Krishnan and J. Liu, Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis, Bioinformatics, 27 (2011), 2686-2691.
doi: 10.1093/bioinformatics/btr454. |
[28] |
, Library of Integrated Network-based Cellular Signatures (LINCS), website,, , ().
|
[29] |
K. Lo, A. E. Raftery, K. M. Dombeck, J. Zhu, E. E. Schadt, R. E. Bumgarner and K. Y. Yeung, Integrating external biological knowledge in the construction of regulatory networks from time-series expression data, BMC Systems Biology, 6 (2012), p101.
doi: 10.1186/1752-0509-6-101. |
[30] |
F. M. Lopes, E. A. de Oliveira and R. M. Cesar, Inference of gene regulatory networks from time series by Tsallis entropy, BMC Systems Biology, 5 (2011), p61.
doi: 10.1186/1752-0509-5-61. |
[31] |
M. J. McGeachie, H. Chang and S. T. Weiss, CGBayesNets: Conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data, PLoS Computational Biology, 10 (2014), e1003676.
doi: 10.1371/journal.pcbi.1003676. |
[32] |
D. Marbach, T. Schaffter, C. Mattiussi and D. Floreano, Generating realistic in silico gene networks for performance assessment of reverse engineering methods, Journal of Computational Biology, 16 (2009), 229-239. |
[33] |
D. Marbach, R. J. Prill, T. Schaffter, C. Mattiussi, D. Floreano and G. Stolovitzky, Revealing strengths and weaknesses of methods for gene network inference, Proceedings of the National Academy of Sciences, 107 (2010), 6286-6291.
doi: 10.1073/pnas.0913357107. |
[34] |
A. A. Margolin, I. Nemenman, K. Basso, C. Wiggins, G. Stolovitzky, R. D. Favera and A. Califano, ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context, BMC Bioinformatics, 7 (2006), S7.
doi: 10.1186/1471-2105-7-S1-S7. |
[35] |
F. Markowetz and R. Spang, Inferring cellular networks: A review, BMC Bioinformatics, 8 (2007), S5.
doi: 10.1186/1471-2105-8-S6-S5. |
[36] |
P. Menéndez, Y. Kourmpetis, C. J. ter Braak and F. A. van Eeuwijk, Gene regulatory networks from multifactorial perturbations using Graphical Lasso: Application to the DREAM4 challenge, PLoS One, 5 (2010), e14147. |
[37] |
P. E. Meyer, K. Kontos, F. Lafitte and G. Bontempi, Information-theoretic inference of large transcriptional regulatory networks, EURASIP Journal on Bioinformatics and Systems Biology, 2007 (2007), 79879.
doi: 10.1155/2007/79879. |
[38] |
P. E. Meyer, F. Lafitte and G. Bontempi, minet: A R/Bioconductor package for inferring large transcriptional networks using mutual information, BMC Bioinformatics, 9 (2008), article 461.
doi: 10.1186/1471-2105-9-461. |
[39] |
G. Michailidis and F. d'Alché-Buc, Autoregressive models for gene regulatory network inference: Sparsity, stability and causality issues, Mathematical Biosciences, 246 (2013), 326-334.
doi: 10.1016/j.mbs.2013.10.003. |
[40] |
K. Murphy and S. Mian, Modelling Gene Expression Data Using Dynamic Bayesian Networks, Vol. 104. Technical report, Computer Science Division, University of California, Berkeley, CA, 1999. |
[41] |
A. Pinna, N. Soranzo and A. De La Fuente, From knockouts to networks: Establishing direct cause-effect relationships through graph analysis, PLoS One, 5 (2010), e12912.
doi: 10.1371/journal.pone.0012912. |
[42] |
A. E. Raftery, D. Madigan and J. A. Hoeting, Bayesian model averaging for linear regression models, Journal of the American Statistical Association, 92 (1997), 179-191.
doi: 10.1080/01621459.1997.10473615. |
[43] |
A. E. Raftery, Bayes factors and BIC, Sociological Methods & Research, 27 (1999), 411-417. |
[44] |
S. Rogers and M. Girolami, A Bayesian regression approach to the inference of regulatory networks from gene expression data, Bioinformatics, 21 (2005), 3131-3137.
doi: 10.1093/bioinformatics/bti487. |
[45] |
F. H. M. Salleh, M. A. Arif, S. Zainudin and M. Firdaus-Raih, Reconstructing gene regulatory networks from knockout data using Gaussian Noise Model and Pearson Correlation Coefficient, Computational Biology and Chemistry, 59 (2015), 3-14. |
[46] |
M. Sanchez-Castillo, I. Tienda-Luna, D. Blanco, M. C. Carrion-Perez and Y. Huang, Bayesian sparse factor model for transcriptional regulatory networks inference, Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European, (2013), 1-4. |
[47] |
A. Sandelin, W. Alkema, P. Engström, W. W. Wasserman and B. Lenhard, JASPAR: an open-access database for eukaryotic transcription factor binding profiles, Nucleic Acids Research, 32 (2004), D91-D94. |
[48] |
M. Scutari, Learning Bayesian Networks with the bnlearn R Package, Journal of Statistical Software, 35 (2010), 1-22. |
[49] |
A. Shojaie and G. Michailidis, Analysis of gene sets based on the underlying regulatory network, Journal of Computational Biology, 16 (2009), 407-426.
doi: 10.1089/cmb.2008.0081. |
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