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Items: 1 to 20 of 86


Network enrichment analysis in complex experiments.

Shojaie A, Michailidis G.

Stat Appl Genet Mol Biol. 2010;9:Article22. doi: 10.2202/1544-6115.1483. Epub 2010 May 22.


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.


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.


Gene expression complex networks: synthesis, identification, and analysis.

Lopes FM, Cesar RM, Costa Lda F.

J Comput Biol. 2011 Oct;18(10):1353-67. doi: 10.1089/cmb.2010.0118. Epub 2011 May 6.


Biological Network Inference and analysis using SEBINI and CABIN.

Taylor R, Singhal M.

Methods Mol Biol. 2009;541:551-76. doi: 10.1007/978-1-59745-243-4_24. Review.


Analysis of gene sets based on the underlying regulatory network.

Shojaie A, Michailidis G.

J Comput Biol. 2009 Mar;16(3):407-26. doi: 10.1089/cmb.2008.0081.


Simultaneous clustering of multiple gene expression and physical interaction datasets.

Narayanan M, Vetta A, Schadt EE, Zhu J.

PLoS Comput Biol. 2010 Apr 15;6(4):e1000742. doi: 10.1371/journal.pcbi.1000742.


Plato's cave algorithm: inferring functional signaling networks from early gene expression shadows.

Shimoni Y, Fink MY, Choi SG, Sealfon SC.

PLoS Comput Biol. 2010 Jun 24;6(6):e1000828. doi: 10.1371/journal.pcbi.1000828.


A flood-based information flow analysis and network minimization method for gene regulatory networks.

Pavlogiannis A, Mozhayskiy V, Tagkopoulos I.

BMC Bioinformatics. 2013 Apr 24;14:137. doi: 10.1186/1471-2105-14-137.


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.


Incorporating existing network information into gene network inference.

Christley S, Nie Q, Xie X.

PLoS One. 2009 Aug 27;4(8):e6799. doi: 10.1371/journal.pone.0006799.


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.


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.


Measurement variation determines the gene network topology reconstructed from experimental data: a case study of the yeast cyclin network.

To CC, Vohradsky J.

FASEB J. 2010 Sep;24(9):3468-78. doi: 10.1096/fj.10-160515. Epub 2010 May 28.


A single source k-shortest paths algorithm to infer regulatory pathways in a gene network.

Shih YK, Parthasarathy S.

Bioinformatics. 2012 Jun 15;28(12):i49-58. doi: 10.1093/bioinformatics/bts212.


IRIS: a method for reverse engineering of regulatory relations in gene networks.

Morganella S, Zoppoli P, Ceccarelli M.

BMC Bioinformatics. 2009 Dec 23;10:444. doi: 10.1186/1471-2105-10-444.


A computational framework for gene regulatory network inference that combines multiple methods and datasets.

Gupta R, Stincone A, Antczak P, Durant S, Bicknell R, Bikfalvi A, Falciani F.

BMC Syst Biol. 2011 Apr 13;5:52. doi: 10.1186/1752-0509-5-52.


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.


Network-free inference of knockout effects in yeast.

Peleg T, Yosef N, Ruppin E, Sharan R.

PLoS Comput Biol. 2010 Jan;6(1):e1000635. doi: 10.1371/journal.pcbi.1000635. Epub 2010 Jan 8.

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