Study of Algorithms Reconstructing Gene Regulatory Network with Resampling and Conditional Mutual Information_Journal of Biomedical Engineering

Authors：

 LIUFei

Institute of Physics and Optoelectronics Technology, Baoji University of Arts and Science, Baoji 721016, China;

Corresponding author：

LIUFei, Email: bwllf@163.com

Keywords：

resampling; conditional mutual information; network reconstruction; gene expression

DOI：

10.7507/1001-5515.20160158

Video：

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Reconstruction of gene regulatory networks (GRNs) from large-scale expression data can mine the potential causality relationship among the genes and help understand the complex regulatory mechanisms. It is of utmost interest and has become a challenging computational problem for understanding the complex regulatory mechanisms in cellular systems. For the past decades, numerous theoretical and computational approaches have been introduced for inferring the GRNs. However, all existing methods of inferring GRNs from gene expression profiles have their strengths and weaknesses. In particular, many properties of GRNs, such as topology sparseness and non-linear dependence, are generally in regulation mechanism but are seldom taken into account simultaneously in one computational method. Some information theory algorithms do not recover the true positive edges that may have been deleted in an earlier computing process. These interaction relationships may reflect the actual relationship of genes. To overcome these disadvantages and to further enhance the precision and robustness of inferred GRNs, we presented an ensemble method, to infer GRNs from gene expression data by adopting two strategies of resampling and arithmetic mean fusion in this work. In this algorithm, the jackknife resampling procedure was first employed to form a series of sub-datasets of gene expression data, then the conditional mutual information was used to generate the corresponding sub-networks from the sub-datasets, and the final GRN was inferred by integrating these sub-networks with an arithmetic mean fusion strategy. Compared with those of the state-of-the-art algorithm on the benchmark synthetic GRNs datasets from the DREAM3 challenge and a real SOS DNA repair network, the results show that our method outperforms significantly LP, LASSO and ARANCE methods, and has a high and robust performance.

Citation： LIU Fei. Study of Algorithms Reconstructing Gene Regulatory Network with Resampling and Conditional Mutual Information. Journal of Biomedical Engineering, 2016, 33(5): 985-990. doi: 10.7507/1001-5515.20160158 Copy

1.	YOUNG W C, RAFTERY A E, YEUNG K Y. Fast Bayesian inference for gene regulatory networks using ScanBMA[J]. BMC Syst Biol, 2014, 8(1):47.
2.	ZOPPOLI P, MORGANELLA S, CECCARELLI M. TimeDelay-ARACNE:Reverse engineering of gene networks from time-course data by an information theoretic approach[J]. BMC Bioinformatics, 2010, 11(1):154.
3.	FAURE E, PETER I S, DAVIDSON E H. A new software package for predictive gene regulatory network modeling and redesign[J]. J Comput Biol, 2013, 20(6):419-423.
4.	NARULA J, WILLIAMS C J, TIWARI A, et al. Mathematical model of a gene regulatory network reconciles effects of genetic perturbations on hematopoietic stem cell emergence[J]. Dev Biol, 2013, 379(2):258-269.
5.	DE SMET R, MARCHAL K. Advantages and limitations of current network inference methods[J]. Nat Rev Microbiol, 2010, 8(10):717-729.
6.	MARBACH D, PRILL R J, SCHAFFTER T, et al. Revealing strengths and weaknesses of methods for gene network inference[J]. Proc Natl Acad Sci U S A, 2010, 107(14):6286-6291.
7.	MARBACH D, COSTELLO J C, KVFFNER R, et al. Wisdom of crowds for robust gene network inference[J]. Nat Methods, 2012, 9(8):796-804.
8.	ZHU Jun, CHEN Yanqing, LEONARDSON A S, et al. Characterizing dynamic changes in the human blood transcriptional network[J]. PLoS Comput Biol, 2010, 6(2):e1000671.
9.	ZOU Min, CONZEN S D. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data[J]. Bioinformatics, 2005, 21(1):71-79.
10.	KIM S, IMOTO S, MIYANO S. Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data[J]. Biosystems, 2004, 75(1-3):57-65.
11.	BANSAL M, DELLA GATTA G, DI BERNARDO D. Inference of gene regulatory networks and compound mode of action from time course gene expression profiles[J]. Bioinformatics, 2006, 22(7):815-822.
12.	LI Zheng, LI Ping, KRISHNAN A, et al. Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis[J]. Bioinformatics, 2011, 27(19):2686-2691.
13.	TIBSHIRANI R. Regression shrinkage and selection via the lasso[J]. J R Stat Soc Series B Stat Methodol, 1996, 58(1):267-288.
14.	TIBSHIRANI R, SAUNDERS M, ROSSET S, et al. Sparsity and smoothness via the fused lasso[J]. J R Stat Soc Series B Stat Methodol, 2005, 67(1):91-108.
15.	ZOU H, HASTIE T. Regularization and variable selection via the elastic net[J]. J R Stat Soc Series B Stat Methodol, 2005, 67(2):301-320.
16.	FRIEDMAN J, HASTIE T, TIBSHIRANI R. Regularization paths for generalized linear models via coordinate descent[J]. J Stat Softw, 2010, 33(1):1-22.
17.	RAFTERY A E, MADIGAN D, HOETING J A. Bayesian model averaging for linear regression models[J]. J Am Stat Assoc, 1997, 92(437):179-191.
18.	MEYER P E, KONTOS K, LAFITTE F, et al. Information-theoretic inference of large transcriptional regulatory networks[J]. EURASIP J Bioinform Syst Biol, 2007, 2007:79879.
19.	FAITH J J, HAYETE B, THADEN J T, et al. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles[J]. PLoS Biol, 2007, 5(1):e8.
20.	ZHANG Xiujun, ZHAO Xingming, HE Kun, et al. Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information[J]. Bioinformatics, 2012, 28(1):98-104.
21.	BEISSER D, BRUNKHORST S, DANDEKAR T, et al. Robustness and accuracy of functional modules in integrated network analysis[J]. Bioinformatics, 2012, 28(14):1887-1894.
22.	SHEN-ORR S S, MILO R, MANGAN S, et al. Network motifs in the transcriptional regulation network of Escherichia coli[J]. Nat Genet, 2002, 31(1):64-68.
23.	RONEN M, ROSENBERG R, SHRAIMAN B I, et al. Assigning numbers to the arrows:parameterizing a gene regulation network by using accurate expression kinetics[J]. Proc Natl Acad Sci U S A, 2002, 99(16):10555-10560.
24.	BASSO K, MARGOLIN A A, STOLOVITZKY G, et al. Reverse engineering of regulatory networks in human B cells[J]. Nat Genet, 2005, 37(4):382-390.
25.	WANG Yong, JOSHI T, ZHANG Xiangsun, et al. Inferring gene regulatory networks from multiple microarray datasets[J]. Bioinformatics, 2006, 22(19):2413-2420.
26.	GEEVEN G, VAN KESTEREN R E, SMIT A B, et al. Identification of context-specific gene regulatory networks with GEMULA——gene expression modeling using LAsso[J]. Bioinformatics, 2012, 28(2):214-221.
27.	MARGOLIN A A, NEMENMAN I, BASSO K, et al. ARACNE:an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context[J]. BMC Bioinformatics, 2006, 7(Suppl 1):S7.

1. YOUNG W C, RAFTERY A E, YEUNG K Y. Fast Bayesian inference for gene regulatory networks using ScanBMA[J]. BMC Syst Biol, 2014, 8(1):47.
2. ZOPPOLI P, MORGANELLA S, CECCARELLI M. TimeDelay-ARACNE:Reverse engineering of gene networks from time-course data by an information theoretic approach[J]. BMC Bioinformatics, 2010, 11(1):154.
3. FAURE E, PETER I S, DAVIDSON E H. A new software package for predictive gene regulatory network modeling and redesign[J]. J Comput Biol, 2013, 20(6):419-423.
4. NARULA J, WILLIAMS C J, TIWARI A, et al. Mathematical model of a gene regulatory network reconciles effects of genetic perturbations on hematopoietic stem cell emergence[J]. Dev Biol, 2013, 379(2):258-269.
5. DE SMET R, MARCHAL K. Advantages and limitations of current network inference methods[J]. Nat Rev Microbiol, 2010, 8(10):717-729.
6. MARBACH D, PRILL R J, SCHAFFTER T, et al. Revealing strengths and weaknesses of methods for gene network inference[J]. Proc Natl Acad Sci U S A, 2010, 107(14):6286-6291.
7. MARBACH D, COSTELLO J C, KVFFNER R, et al. Wisdom of crowds for robust gene network inference[J]. Nat Methods, 2012, 9(8):796-804.
8. ZHU Jun, CHEN Yanqing, LEONARDSON A S, et al. Characterizing dynamic changes in the human blood transcriptional network[J]. PLoS Comput Biol, 2010, 6(2):e1000671.
9. ZOU Min, CONZEN S D. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data[J]. Bioinformatics, 2005, 21(1):71-79.
10. KIM S, IMOTO S, MIYANO S. Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data[J]. Biosystems, 2004, 75(1-3):57-65.
11. BANSAL M, DELLA GATTA G, DI BERNARDO D. Inference of gene regulatory networks and compound mode of action from time course gene expression profiles[J]. Bioinformatics, 2006, 22(7):815-822.
12. LI Zheng, LI Ping, KRISHNAN A, et al. Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis[J]. Bioinformatics, 2011, 27(19):2686-2691.
13. TIBSHIRANI R. Regression shrinkage and selection via the lasso[J]. J R Stat Soc Series B Stat Methodol, 1996, 58(1):267-288.
14. TIBSHIRANI R, SAUNDERS M, ROSSET S, et al. Sparsity and smoothness via the fused lasso[J]. J R Stat Soc Series B Stat Methodol, 2005, 67(1):91-108.
15. ZOU H, HASTIE T. Regularization and variable selection via the elastic net[J]. J R Stat Soc Series B Stat Methodol, 2005, 67(2):301-320.
16. FRIEDMAN J, HASTIE T, TIBSHIRANI R. Regularization paths for generalized linear models via coordinate descent[J]. J Stat Softw, 2010, 33(1):1-22.
17. RAFTERY A E, MADIGAN D, HOETING J A. Bayesian model averaging for linear regression models[J]. J Am Stat Assoc, 1997, 92(437):179-191.
18. MEYER P E, KONTOS K, LAFITTE F, et al. Information-theoretic inference of large transcriptional regulatory networks[J]. EURASIP J Bioinform Syst Biol, 2007, 2007:79879.
19. FAITH J J, HAYETE B, THADEN J T, et al. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles[J]. PLoS Biol, 2007, 5(1):e8.
20. ZHANG Xiujun, ZHAO Xingming, HE Kun, et al. Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information[J]. Bioinformatics, 2012, 28(1):98-104.
21. BEISSER D, BRUNKHORST S, DANDEKAR T, et al. Robustness and accuracy of functional modules in integrated network analysis[J]. Bioinformatics, 2012, 28(14):1887-1894.
22. SHEN-ORR S S, MILO R, MANGAN S, et al. Network motifs in the transcriptional regulation network of Escherichia coli[J]. Nat Genet, 2002, 31(1):64-68.
23. RONEN M, ROSENBERG R, SHRAIMAN B I, et al. Assigning numbers to the arrows:parameterizing a gene regulation network by using accurate expression kinetics[J]. Proc Natl Acad Sci U S A, 2002, 99(16):10555-10560.
24. BASSO K, MARGOLIN A A, STOLOVITZKY G, et al. Reverse engineering of regulatory networks in human B cells[J]. Nat Genet, 2005, 37(4):382-390.
25. WANG Yong, JOSHI T, ZHANG Xiangsun, et al. Inferring gene regulatory networks from multiple microarray datasets[J]. Bioinformatics, 2006, 22(19):2413-2420.
26. GEEVEN G, VAN KESTEREN R E, SMIT A B, et al. Identification of context-specific gene regulatory networks with GEMULA——gene expression modeling using LAsso[J]. Bioinformatics, 2012, 28(2):214-221.
27. MARGOLIN A A, NEMENMAN I, BASSO K, et al. ARACNE:an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context[J]. BMC Bioinformatics, 2006, 7(Suppl 1):S7.

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