title: Application of centrality measures in the identification of critical genes in diabetes mellitus.
abstract: The connectivity of a protein and its structure is related to its functional properties. Many experimental approaches have been employed for the identification of Diabetes Mellitus (DM) associated candidate genes. Therefore, it is of interest to use var ious graph centrality measures integrated with the genes associated with the human Diabetes Mellitus network for the identification of potential targets. We used 2728 genes known to cause Diabetes Mellitus from Jensenlab (Novo Nordisk Foundation Center for Protein Research, Denmark) for this analysis. A protein-protein interaction network was further constructed using a tool Centralities in Biological Networks (CentiBiN) with 1020 nodes after eliminating the duplicates, parallel edges, self -loop edges and unknown Human Protein Reference Database (HPRD) IDS. We used fourteen centralities measures which are useful in identifying the structural characteristic of individuals in the network. The results of the centrality measures are highly correlated. Thus, we identified genes that are critically associated with DM. We further report the top ten genes of all fourteen centrality measures for further consideration as targets for DM.
authors.affiliation: S R K Institute of Technology, Vijayawada.
authors.forename: Chintagunta
authors.initials: C
authors.lastname: Ambedkar
authors.affiliation: Krishna University, Machilipatnam.
authors.forename: Kiran Kumar
authors.initials: KK
authors.lastname: Reddi
authors.affiliation: C R Rao AIMSCS, Hyderabad.
authors.forename: Naresh Babu
authors.initials: NB
authors.lastname: Muppalaneni
authors.affiliation: C R Rao AIMSCS, Hyderabad.
authors.forename: Duggineni
authors.initials: D
authors.lastname: Kalyani
references.citation: J Theor Biol. 2003 Jul 7;223(1):45-53
references.pmid: 12782116
references.citation: BMC Bioinformatics. 2006 Apr 21;7:219
references.pmid: 16630347
references.citation: Nucleic Acids Res. 2009 Jan;37(Database issue):D767-72
references.pmid: 18988627
references.citation: Bioinformatics. 2003 Mar 1;19(4):532-8
references.pmid: 12611809
references.citation: Nucleic Acids Res. 2000 Jan 1;28(1):289-91
references.pmid: 10592249
references.citation: Hypertens Res. 2008 Feb;31(2):173-4
references.pmid: 18360034
references.citation: PLoS Comput Biol. 2008 Nov;4(11):e1000237
references.pmid: 19043542
references.citation: BMC Bioinformatics. 2009 Feb 27;10:73
references.pmid: 19245720
references.citation: Diabetes. 1979 Dec;28(12):1039-57
references.pmid: 510803
references.citation: Nature. 2001 May 3;411(6833):41-2
references.pmid: 11333967
references.citation: Brief Bioinform. 2007 Sep;8(5):333-46
references.pmid: 17638813
references.citation: Nucleic Acids Res. 2009 Jul;37(Web Server issue):W305-11
references.pmid: 19465376
references.citation: Nat Rev Genet. 2011 Jan;12(1):56-68
references.pmid: 21164525
references.citation: Nucleic Acids Res. 2007 Jan;35(Database issue):D572-4
references.pmid: 17135203
references.citation: Cell. 2005 Sep 23;122(6):957-68
references.pmid: 16169070
references.citation: Indian J Occup Environ Med. 2009 Dec;13(3):109-12
references.pmid: 20442827
references.citation: Mol Biosyst. 2013 Aug;9(8):2163-7
references.pmid: 23728082
references.citation: Nucleic Acids Res. 2009 Feb;37(2):622-8
references.pmid: 19073697
references.citation: Nat Biotechnol. 2000 Nov;18(11):1121-2
references.pmid: 11062388
references.citation: Adv Protein Chem. 1970;24:343-446
references.pmid: 4916268
references.citation: Science. 2002 Oct 25;298(5594):824-7
references.pmid: 12399590