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Items: 1 to 20 of 80

1.

Directing experimental biology: a case study in mitochondrial biogenesis.

Hibbs MA, Myers CL, Huttenhower C, Hess DC, Li K, Caudy AA, Troyanskaya OG.

PLoS Comput Biol. 2009 Mar;5(3):e1000322. doi: 10.1371/journal.pcbi.1000322. Epub 2009 Mar 20.

2.

Systematic planning of genome-scale experiments in poorly studied species.

Guan Y, Dunham M, Caudy A, Troyanskaya O.

PLoS Comput Biol. 2010 Mar 5;6(3):e1000698. doi: 10.1371/journal.pcbi.1000698.

3.
4.

An in silico approach combined with in vivo experiments enables the identification of a new protein whose overexpression can compensate for specific respiratory defects in Saccharomyces cerevisiae.

Glatigny A, Mathieu L, Herbert CJ, Dujardin G, Meunier B, Mucchielli-Giorgi MH.

BMC Syst Biol. 2011 Oct 25;5:173. doi: 10.1186/1752-0509-5-173.

5.
6.

Joint learning of gene functions--a Bayesian network model approach.

Deng X, Geng H, Ali HH.

J Bioinform Comput Biol. 2006 Apr;4(2):217-39.

PMID:
16819781
7.

Protein complexes are central in the yeast genetic landscape.

Michaut M, Baryshnikova A, Costanzo M, Myers CL, Andrews BJ, Boone C, Bader GD.

PLoS Comput Biol. 2011 Feb;7(2):e1001092. doi: 10.1371/journal.pcbi.1001092. Epub 2011 Feb 24.

8.

Molecular machinery of mitochondrial dynamics in yeast.

Merz S, Hammermeister M, Altmann K, Dürr M, Westermann B.

Biol Chem. 2007 Sep;388(9):917-26. Review.

PMID:
17696775
9.

Does the study of genetic interactions help predict the function of mitochondrial proteins in Saccharomyces cerevisiae?

Ostojić J, Glatigny A, Herbert CJ, Dujardin G, Bonnefoy N.

Biochimie. 2014 May;100:27-37. doi: 10.1016/j.biochi.2013.11.004. Epub 2013 Nov 19. Review.

PMID:
24262604
10.

Modelling the network of cell cycle transcription factors in the yeast Saccharomyces cerevisiae.

Cokus S, Rose S, Haynor D, Grønbech-Jensen N, Pellegrini M.

BMC Bioinformatics. 2006 Aug 16;7:381.

11.

Predicting gene function in a hierarchical context with an ensemble of classifiers.

Guan Y, Myers CL, Hess DC, Barutcuoglu Z, Caudy AA, Troyanskaya OG.

Genome Biol. 2008;9 Suppl 1:S3. doi: 10.1186/gb-2008-9-s1-s3. Epub 2008 Jun 27.

12.
13.

Detecting functional modules in the yeast protein-protein interaction network.

Chen J, Yuan B.

Bioinformatics. 2006 Sep 15;22(18):2283-90. Epub 2006 Jul 12.

PMID:
16837529
14.

Iterative orthology prediction uncovers new mitochondrial proteins and identifies C12orf62 as the human ortholog of COX14, a protein involved in the assembly of cytochrome c oxidase.

Szklarczyk R, Wanschers BF, Cuypers TD, Esseling JJ, Riemersma M, van den Brand MA, Gloerich J, Lasonder E, van den Heuvel LP, Nijtmans LG, Huynen MA.

Genome Biol. 2012 Feb 22;13(2):R12. doi: 10.1186/gb-2012-13-2-r12.

15.

The impact of incomplete knowledge on evaluation: an experimental benchmark for protein function prediction.

Huttenhower C, Hibbs MA, Myers CL, Caudy AA, Hess DC, Troyanskaya OG.

Bioinformatics. 2009 Sep 15;25(18):2404-10. doi: 10.1093/bioinformatics/btp397. Epub 2009 Jun 26.

16.
17.

Learning yeast gene functions from heterogeneous sources of data using hybrid weighted Bayesian networks.

Deng X, Geng H, Ali H.

Proc IEEE Comput Syst Bioinform Conf. 2005:25-34.

PMID:
16447959
18.
19.

Functional topology in a network of protein interactions.

Przulj N, Wigle DA, Jurisica I.

Bioinformatics. 2004 Feb 12;20(3):340-8.

PMID:
14960460
20.

Predicting gene function through systematic analysis and quality assessment of high-throughput data.

Kemmeren P, Kockelkorn TT, Bijma T, Donders R, Holstege FC.

Bioinformatics. 2005 Apr 15;21(8):1644-52. Epub 2004 Nov 5.

PMID:
15531615

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