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Results: 1 to 20 of 131

Similar articles for PubMed (Select 22526920)

1.

Is newer better?--evaluating the effects of data curation on integrated analyses in Saccharomyces cerevisiae.

James K, Wipat A, Hallinan J.

Integr Biol (Camb). 2012 Jul;4(7):715-27. doi: 10.1039/c2ib00123c. Epub 2012 Apr 23.

PMID:
22526920
2.

Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae.

Reguly T, Breitkreutz A, Boucher L, Breitkreutz BJ, Hon GC, Myers CL, Parsons A, Friesen H, Oughtred R, Tong A, Stark C, Ho Y, Botstein D, Andrews B, Boone C, Troyanskya OG, Ideker T, Dolinski K, Batada NN, Tyers M.

J Biol. 2006;5(4):11. Epub 2006 Jun 8.

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Bayesian data integration: a functional perspective.

Huttenhower C, Troyanskaya OG.

Comput Syst Bioinformatics Conf. 2006:341-51.

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A probabilistic graph-theoretic approach to integrate multiple predictions for the protein-protein subnetwork prediction challenge.

Chua HN, Hugo W, Liu G, Li X, Wong L, Ng SK.

Ann N Y Acad Sci. 2009 Mar;1158:224-33. doi: 10.1111/j.1749-6632.2008.03760.x.

PMID:
19348644
9.

A matrix based algorithm for Protein-Protein Interaction prediction using Domain-Domain Associations.

Binny Priya S, Saha S, Anishetty R, Anishetty S.

J Theor Biol. 2013 Jun 7;326:36-42. doi: 10.1016/j.jtbi.2013.02.016. Epub 2013 Mar 6.

PMID:
23473859
10.

High-throughput analyses and curation of protein interactions in yeast.

Wodak SJ, Vlasblom J, Pu S.

Methods Mol Biol. 2011;759:381-406. doi: 10.1007/978-1-61779-173-4_22.

PMID:
21863499
11.

Are scale-free networks robust to measurement errors?

Lin N, Zhao H.

BMC Bioinformatics. 2005 May 16;6:119.

12.

MPact: the MIPS protein interaction resource on yeast.

Güldener U, Münsterkötter M, Oesterheld M, Pagel P, Ruepp A, Mewes HW, Stümpflen V.

Nucleic Acids Res. 2006 Jan 1;34(Database issue):D436-41.

13.

Pushing structural information into the yeast interactome by high-throughput protein docking experiments.

Mosca R, Pons C, Fernández-Recio J, Aloy P.

PLoS Comput Biol. 2009 Aug;5(8):e1000490. doi: 10.1371/journal.pcbi.1000490. Epub 2009 Aug 28.

14.

A probabilistic functional network of yeast genes.

Lee I, Date SV, Adai AT, Marcotte EM.

Science. 2004 Nov 26;306(5701):1555-8.

15.

A statistical framework for combining and interpreting proteomic datasets.

Gilchrist MA, Salter LA, Wagner A.

Bioinformatics. 2004 Mar 22;20(5):689-700. Epub 2004 Jan 22.

16.

Integrating diverse biological and computational sources for reliable protein-protein interactions.

Wu M, Li X, Chua HN, Kwoh CK, Ng SK.

BMC Bioinformatics. 2010 Oct 15;11 Suppl 7:S8. doi: 10.1186/1471-2105-11-S7-S8.

17.

Function-function correlated multi-label protein function prediction over interaction networks.

Wang H, Huang H, Ding C.

J Comput Biol. 2013 Apr;20(4):322-43. doi: 10.1089/cmb.2012.0272.

PMID:
23560867
18.

Bayesian Markov Random Field analysis for protein function prediction based on network data.

Kourmpetis YA, van Dijk AD, Bink MC, van Ham RC, ter Braak CJ.

PLoS One. 2010 Feb 24;5(2):e9293. doi: 10.1371/journal.pone.0009293.

19.

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
20.

New insights into protein-protein interaction data lead to increased estimates of the S. cerevisiae interactome size.

Sambourg L, Thierry-Mieg N.

BMC Bioinformatics. 2010 Dec 21;11:605. doi: 10.1186/1471-2105-11-605.

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