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Items: 1 to 20 of 116

1.

Maximal extraction of biological information from genetic interaction data.

Carter GW, Galas DJ, Galitski T.

PLoS Comput Biol. 2009 Apr;5(4):e1000347. doi: 10.1371/journal.pcbi.1000347. Epub 2009 Apr 3.

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3.

A hybrid graph-theoretic method for mining overlapping functional modules in large sparse protein interaction networks.

Zhang S, Liu HW, Ning XM, Zhang XS.

Int J Data Min Bioinform. 2009;3(1):68-84.

PMID:
19432377
4.

Selective integration of multiple biological data for supervised network inference.

Kato T, Tsuda K, Asai K.

Bioinformatics. 2005 May 15;21(10):2488-95. Epub 2005 Feb 22.

5.

A systems-biology approach to modular genetic complexity.

Carter GW, Rush CG, Uygun F, Sakhanenko NA, Galas DJ, Galitski T.

Chaos. 2010 Jun;20(2):026102. doi: 10.1063/1.3455183. Review.

6.

Supervised inference of gene-regulatory networks.

To CC, Vohradsky J.

BMC Bioinformatics. 2008 Jan 4;9:2. doi: 10.1186/1471-2105-9-2.

7.

Modularized learning of genetic interaction networks from biological annotations and mRNA expression data.

Lee PH, Lee D.

Bioinformatics. 2005 Jun 1;21(11):2739-47. Epub 2005 Mar 29.

8.

Systematic interpretation of genetic interactions using protein networks.

Kelley R, Ideker T.

Nat Biotechnol. 2005 May;23(5):561-6.

9.

Nested effects models for high-dimensional phenotyping screens.

Markowetz F, Kostka D, Troyanskaya OG, Spang R.

Bioinformatics. 2007 Jul 1;23(13):i305-12.

10.

From pull-down data to protein interaction networks and complexes with biological relevance.

Zhang B, Park BH, Karpinets T, Samatova NF.

Bioinformatics. 2008 Apr 1;24(7):979-86. doi: 10.1093/bioinformatics/btn036. Epub 2008 Feb 26.

11.

NIBBS-search for fast and accurate prediction of phenotype-biased metabolic systems.

Schmidt MC, Rocha AM, Padmanabhan K, Shpanskaya Y, Banfield J, Scott K, Mihelcic JR, Samatova NF.

PLoS Comput Biol. 2012;8(5):e1002490. doi: 10.1371/journal.pcbi.1002490. Epub 2012 May 10.

12.

A parallel algorithm for reverse engineering of biological networks.

Bazil JN, Qi F, Beard DA.

Integr Biol (Camb). 2011 Dec;3(12):1215-23. doi: 10.1039/c1ib00117e. Epub 2011 Nov 14.

13.

ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining.

Huan T, Sivachenko AY, Harrison SH, Chen JY.

BMC Bioinformatics. 2008 Aug 12;9 Suppl 9:S5. doi: 10.1186/1471-2105-9-S9-S5.

14.

Topology of gene expression networks as revealed by data mining and modeling.

Lukashin AV, Lukashev ME, Fuchs R.

Bioinformatics. 2003 Oct 12;19(15):1909-16.

15.

LICORN: learning cooperative regulation networks from gene expression data.

Elati M, Neuvial P, Bolotin-Fukuhara M, Barillot E, Radvanyi F, Rouveirol C.

Bioinformatics. 2007 Sep 15;23(18):2407-14. Epub 2007 Aug 24.

16.

Integrated analysis of multiple data sources reveals modular structure of biological networks.

Lu H, Shi B, Wu G, Zhang Y, Zhu X, Zhang Z, Liu C, Zhao Y, Wu T, Wang J, Chen R.

Biochem Biophys Res Commun. 2006 Jun 23;345(1):302-9. Epub 2006 Apr 27.

PMID:
16690033
17.

Functional annotation from predicted protein interaction networks.

McDermott J, Bumgarner R, Samudrala R.

Bioinformatics. 2005 Aug 1;21(15):3217-26. Epub 2005 May 26.

18.

A framework for elucidating regulatory networks based on prior information and expression data.

Gevaert O, Van Vooren S, De Moor B.

Ann N Y Acad Sci. 2007 Dec;1115:240-8. Epub 2007 Oct 9.

PMID:
17925352
19.

Inferring gene regulatory networks with time delays using a genetic algorithm.

Wu FX, Poirier GG, Zhang WJ.

Syst Biol (Stevenage). 2005 Jun;152(2):67-74.

PMID:
17044234
20.

Comparative analysis of protein interaction networks.

Bork P.

Bioinformatics. 2002;18 Suppl 2:S64.

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