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Results: 1 to 20 of 33

1.

A quick guide for building a successful bioinformatics community.

Budd A, Corpas M, Brazas MD, Fuller JC, Goecks J, Mulder NJ, Michaut M, Ouellette BF, Pawlik A, Blomberg N.

PLoS Comput Biol. 2015 Feb 5;11(2):e1003972. doi: 10.1371/journal.pcbi.1003972. eCollection 2015 Feb.

2.

Information content-based Gene Ontology functional similarity measures: which one to use for a given biological data type?

Mazandu GK, Mulder NJ.

PLoS One. 2014 Dec 4;9(12):e113859. doi: 10.1371/journal.pone.0113859. eCollection 2014.

3.

Using biological networks to improve our understanding of infectious diseases.

Mulder NJ, Akinola RO, Mazandu GK, Rapanoel H.

Comput Struct Biotechnol J. 2014 Aug 27;11(18):1-10. doi: 10.1016/j.csbj.2014.08.006. eCollection 2014 Aug. Review.

4.

The use of semantic similarity measures for optimally integrating heterogeneous Gene Ontology data from large scale annotation pipelines.

Mazandu GK, Mulder NJ.

Front Genet. 2014 Aug 6;5:264. doi: 10.3389/fgene.2014.00264. eCollection 2014.

5.

Bioinformatics education--perspectives and challenges out of Africa.

Tastan Bishop Ö, Adebiyi EF, Alzohairy AM, Everett D, Ghedira K, Ghouila A, Kumuthini J, Mulder NJ, Panji S, Patterton HG; H3ABioNet Consortium; H3Africa Consortium.

Brief Bioinform. 2015 Mar;16(2):355-64. doi: 10.1093/bib/bbu022. Epub 2014 Jul 2.

6.

A web-based protein interaction network visualizer.

Salazar GA, Meintjes A, Mazandu GK, Rapanoël HA, Akinola RO, Mulder NJ.

BMC Bioinformatics. 2014 May 6;15:129. doi: 10.1186/1471-2105-15-129.

7.

Information content-based gene ontology semantic similarity approaches: toward a unified framework theory.

Mazandu GK, Mulder NJ.

Biomed Res Int. 2013;2013:292063. doi: 10.1155/2013/292063. Epub 2013 Sep 2.

8.

DaGO-Fun: tool for Gene Ontology-based functional analysis using term information content measures.

Mazandu GK, Mulder NJ.

BMC Bioinformatics. 2013 Sep 25;14:284. doi: 10.1186/1471-2105-14-284.

9.

Determining ancestry proportions in complex admixture scenarios in South Africa using a novel proxy ancestry selection method.

Chimusa ER, Daya M, Möller M, Ramesar R, Henn BM, van Helden PD, Mulder NJ, Hoal EG.

PLoS One. 2013 Sep 16;8(9):e73971. doi: 10.1371/journal.pone.0073971. eCollection 2013.

10.

Genome-wide association study of ancestry-specific TB risk in the South African Coloured population.

Chimusa ER, Zaitlen N, Daya M, Möller M, van Helden PD, Mulder NJ, Price AL, Hoal EG.

Hum Mol Genet. 2014 Feb 1;23(3):796-809. doi: 10.1093/hmg/ddt462. Epub 2013 Sep 20.

11.

Predicting and analyzing interactions between Mycobacterium tuberculosis and its human host.

Rapanoel HA, Mazandu GK, Mulder NJ.

PLoS One. 2013 Jul 2;8(7):e67472. doi: 10.1371/journal.pone.0067472. Print 2013.

12.

Co-infection with Mycobacterium tuberculosis and human immunodeficiency virus: an overview and motivation for systems approaches.

Deffur A, Mulder NJ, Wilkinson RJ.

Pathog Dis. 2013 Nov;69(2):101-13. doi: 10.1111/2049-632X.12060. Epub 2013 Jul 17. Review.

13.

Function prediction and analysis of mycobacterium tuberculosis hypothetical proteins.

Mazandu GK, Mulder NJ.

Int J Mol Sci. 2012;13(6):7283-302. doi: 10.3390/ijms13067283. Epub 2012 Jun 13.

14.

A topology-based metric for measuring term similarity in the gene ontology.

Mazandu GK, Mulder NJ.

Adv Bioinformatics. 2012;2012:975783. doi: 10.1155/2012/975783. Epub 2012 May 15.

15.

Generation and Analysis of Large-Scale Data-Driven Mycobacterium tuberculosis Functional Networks for Drug Target Identification.

Mazandu GK, Mulder NJ.

Adv Bioinformatics. 2011;2011:801478. doi: 10.1155/2011/801478. Epub 2011 Nov 29.

16.

Using the underlying biological organization of the Mycobacterium tuberculosis functional network for protein function prediction.

Mazandu GK, Mulder NJ.

Infect Genet Evol. 2012 Jul;12(5):922-32. doi: 10.1016/j.meegid.2011.10.027. Epub 2011 Nov 7.

PMID:
22085822
17.

Scoring protein relationships in functional interaction networks predicted from sequence data.

Mazandu GK, Mulder NJ.

PLoS One. 2011 Apr 19;6(4):e18607. doi: 10.1371/journal.pone.0018607.

18.

Contribution of microarray data to the advancement of knowledge on the Mycobacterium tuberculosis interactome: use of the random partial least squares approach.

Mazandu GK, Opap K, Mulder NJ.

Infect Genet Evol. 2011 Jun;11(4):725-33. doi: 10.1016/j.meegid.2011.04.012. Epub 2011 Apr 14.

PMID:
21514402
19.

Investigating the effect of paralogs on microarray gene-set analysis.

Faure AJ, Seoighe C, Mulder NJ.

BMC Bioinformatics. 2011 Jan 24;12:29. doi: 10.1186/1471-2105-12-29.

20.

Contribution of microarray data to the advancement of knowledge on the Mycobacterium tuberculosis interactome: use of the random partial least squares approach.

Mazandu GK, Opap K, Mulder NJ.

Infect Genet Evol. 2011 Jan;11(1):181-9. doi: 10.1016/j.meegid.2010.09.003. Epub 2010 Sep 17.

PMID:
20850566
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