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1.

OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks.

Lim N, Senbabaoglu Y, Michailidis G, d'Alché-Buc F.

Bioinformatics. 2013 Jun 1;29(11):1416-23. doi: 10.1093/bioinformatics/btt167. Epub 2013 Apr 10.

2.

Inferring gene regulatory networks via nonlinear state-space models and exploiting sparsity.

Noor A, Serpedin E, Nounou M, Nounou HN.

IEEE/ACM Trans Comput Biol Bioinform. 2012 Jul-Aug;9(4):1203-11. doi: 10.1109/TCBB.2012.32.

PMID:
22350207
3.

An algebra-based method for inferring gene regulatory networks.

Vera-Licona P, Jarrah A, Garcia-Puente LD, McGee J, Laubenbacher R.

BMC Syst Biol. 2014 Mar 26;8:37. doi: 10.1186/1752-0509-8-37.

4.

An integer optimization algorithm for robust identification of non-linear gene regulatory networks.

Chemmangattuvalappil N, Task K, Banerjee I.

BMC Syst Biol. 2012 Sep 2;6:119.

5.

Inferring genetic interactions via a nonlinear model and an optimization algorithm.

Chen CM, Lee C, Chuang CL, Wang CC, Shieh GS.

BMC Syst Biol. 2010 Feb 26;4:16. doi: 10.1186/1752-0509-4-16.

6.

TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach.

Zoppoli P, Morganella S, Ceccarelli M.

BMC Bioinformatics. 2010 Mar 25;11:154. doi: 10.1186/1471-2105-11-154.

7.

Functional data analysis for identifying nonlinear models of gene regulatory networks.

Summer G, Perkins TJ.

BMC Genomics. 2010 Dec 2;11 Suppl 4:S18. doi: 10.1186/1471-2164-11-S4-S18.

8.

Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis.

Li Z, Li P, Krishnan A, Liu J.

Bioinformatics. 2011 Oct 1;27(19):2686-91. doi: 10.1093/bioinformatics/btr454. Epub 2011 Aug 4.

9.

Reverse engineering module networks by PSO-RNN hybrid modeling.

Zhang Y, Xuan J, de los Reyes BG, Clarke R, Ressom HW.

BMC Genomics. 2009 Jul 7;10 Suppl 1:S15. doi: 10.1186/1471-2164-10-S1-S15.

10.

A novel gene network inference algorithm using predictive minimum description length approach.

Chaitankar V, Ghosh P, Perkins EJ, Gong P, Deng Y, Zhang C.

BMC Syst Biol. 2010 May 28;4 Suppl 1:S7. doi: 10.1186/1752-0509-4-S1-S7.

11.

Structural systems identification of genetic regulatory networks.

Xiong H, Choe Y.

Bioinformatics. 2008 Feb 15;24(4):553-60. doi: 10.1093/bioinformatics/btm623. Epub 2008 Jan 5.

12.

Time lagged information theoretic approaches to the reverse engineering of gene regulatory networks.

Chaitankar V, Ghosh P, Perkins EJ, Gong P, Zhang C.

BMC Bioinformatics. 2010 Oct 7;11 Suppl 6:S19. doi: 10.1186/1471-2105-11-S6-S19.

13.

Inference of gene regulatory networks from genome-wide knockout fitness data.

Wang L, Wang X, Arkin AP, Samoilov MS.

Bioinformatics. 2013 Feb 1;29(3):338-46. doi: 10.1093/bioinformatics/bts634. Epub 2012 Dec 27.

14.

GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods.

Schaffter T, Marbach D, Floreano D.

Bioinformatics. 2011 Aug 15;27(16):2263-70. doi: 10.1093/bioinformatics/btr373. Epub 2011 Jun 22.

15.

Inference of gene regulatory networks from time series by Tsallis entropy.

Lopes FM, de Oliveira EA, Cesar RM Jr.

BMC Syst Biol. 2011 May 5;5:61. doi: 10.1186/1752-0509-5-61.

16.

Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations.

Cai X, Bazerque JA, Giannakis GB.

PLoS Comput Biol. 2013;9(5):e1003068. doi: 10.1371/journal.pcbi.1003068. Epub 2013 May 23.

17.

A computational framework for qualitative simulation of nonlinear dynamical models of gene-regulatory networks.

Ironi L, Panzeri L.

BMC Bioinformatics. 2009 Oct 15;10 Suppl 12:S14. doi: 10.1186/1471-2105-10-S12-S14.

18.

An extended Kalman filtering approach to modeling nonlinear dynamic gene regulatory networks via short gene expression time series.

Wang Z, Liu X, Liu Y, Liang J, Vinciotti V.

IEEE/ACM Trans Comput Biol Bioinform. 2009 Jul-Sep;6(3):410-9. doi: 10.1109/TCBB.2009.5.

PMID:
19644169
19.

Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data.

Kim J, Bates DG, Postlethwaite I, Heslop-Harrison P, Cho KH.

Bioinformatics. 2008 May 15;24(10):1286-92. doi: 10.1093/bioinformatics/btn107. Epub 2008 Mar 26.

20.

DREAM3: network inference using dynamic context likelihood of relatedness and the inferelator.

Madar A, Greenfield A, Vanden-Eijnden E, Bonneau R.

PLoS One. 2010 Mar 22;5(3):e9803. doi: 10.1371/journal.pone.0009803.

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