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[2] H E & U M Tuomilehto J, Indstrom J, Eriksson J, Valle T. Numb Er 18 Prevention of Type 2 Diabetes Mellitus By Changes in Lifestyle Among Subjects With Impaired Glucose Tolerance.N. Engl J Med. 2001; 344(18): 1343–1350.
[3] H Huang, X Wu, R Pandey, J Li, G Zhao, S Ibrahim, J Y Chen. C2Maps: a network pharmacology database with comprehensive disease-gene-drug connectivity relationships.BMC Genomics.2012; 13(6): S17.
[4] J Calles-Escandon,M Cipolla. Diabetes and endothelial dysfunction: a clinical perspective.Endocr Rev.2001; 22: 36–52.
[5] J Li, X Zhu, J Y Chen. Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts.PLoS Comput Biol.2009; 5(7): e1000450.
[6] S Dow, A Lucau-danila, K Anderson, A P Arkin, A Astromoff, M El Bakkoury, R Bangham, R Benito, S Brachat, B Andre, D F Jaramillo, D E Kelly, S L Kelly, P Ko. Functional profiling of the Saccharomyces cerevisiae genome.Nature.2002; 418(6896): 387–91.
[7] L M Cullen, G M Arndt, A T Park, N S Wales. Genome-wide screening for gene function using RNAi in mammalian cells.Immunol Cell Biol.2005; 83: 217–223.
[8] T Roemer, B Jiang, J Davison, T Ketela, K Veillette, A Breton, F Tandia, A Linteau, S Sillaots, C Marta, N Martel, S Veronneau, S Lemieux, S Kauffman, J Becker, R Storms, C Boone, H Bussey. Large-scale essential gene identification in Candida albicans and applications to antifungal drug discovery.Mol. Microbiol.2003; 50(1): 167–181.
[9] M Li, H Zhang, J Wang, Y Pan. A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data. BMC Syst. Biol. 2012; 6(1): 15.
[10] J Ran, H Li, J Fu, L Liu, Y Xing, X Li, H Shen, Y Chen, X Jiang, Y Li, H Li. Construction and analysis of the protein-protein interaction network related to essential hypertension. BMC Syst. Biol.2013; 7: 32.
[11] L J Jensen, M Kuhn, M Stark, S Chaffron, C Creevey, J Muller, T Doerks, P Julien, A Roth, M Simonovic, P Bork, C von Mering. STRING 8--a global view on proteins and their functional interactions in 630 organisms.Nucleic Acids Res.2009; 37: D412–D416.
[12] H D Herce, W Deng, J Helma, H Leonhardt, M C Cardoso.Interactions in living cells.Nat Commun.2013; 4: 1–8.
[13] S Jones, J M Thornton. Principles of protein-protein interactions. Proc Natl Acad Sci U S A.1996; 93(1): 13–20.
[14] E M Phizicky, S Fields. Protein-protein interactions: Methods for detection and analysis.Microbiol Rev.1995; 59(1): 94–123.
[15] Y Xia, H Yu, R Jansen, M Seringhaus, S Baxter, D Greenbaum, H Zhao, M Gerstein. Analyzing Cellular Biochemistry In Terms of Molecular Networks.Annu Rev Biochem. 2004; 73: 1051–87.
[16] P Shannon, A Markiel, O Ozier, N S Baliga, J T Wang, D Ramage, N Amin, B Schwikowski, T Ideker. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks.Cold Spring Harb Lab Pres. 2003; 13: 2498–2504.
[17] S Hwang, SW Son, S C Kim, Y J Kim, H Jeong, D Lee. A protein interaction network associated with asthma.J Theor Biol.2008; 252(4): 722–731.
[18] L C Freeman. Centered Graphs and The Structure of Ego Networks.Math Soc Sci.1982; 3: 291–304.
[19] L C Freeman. Centrality in Social Networks Conceptual Clarification.Elsevier.1978; 1: 215–239.
[20] J Goñi, F J Esteban, N de Mendizábal, J Sepulcre, S Ardanza-Trevijano, I Agirrezabal, P Villoslada. A computational analysis of protein-protein interaction networks in neurodegenerative diseases.BMC Syst. Biol. 2008; 2(52).
[21] M Kim, K Kwon, M L Cyberpsychology. Psychological Characteristics of Internet Dating Service Users: The Effect of Self-Esteem, Involvement, and Sociability on the Use of Internet Dating Services.Cyberpsychol Behav.2009; 12(4): 445–449.
[22] G Lima-mendez, J Van Helden. The powerful law of the power law and other myths in network biology.Mol Biosyst.2009; 5(12):1482-93.
[23] J D J Han, N Bertin, T Hao, D S Goldberg, G F Berriz, L V Zhang, D Dupuy, A J M Walhout, M E Cusick, F P Roth, M Vida. Evidence for dynamically organized modularity in the yeast protein- protein interaction network. Nature. 2004; 430: 88–93.
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Affiliations
Muhammad Syafiuddin Usman
Department of Computer Science, Bogor Agricultural University
Wisnu Ananta Kusuma
Department of Computer Science Bogor Agricultural University
Farit Mochamad Afendi
Department of Statistics, Bogor Agricultural University
Rudi Heryanto
Tropical Biopharmaca Research Center, Bogor Agricultural University
Identification of Significant Proteins Associated with Diabetes Mellitus Using Network Analysis of Protein-Protein Interactions
Abstract
Computation approach to identify significance of proteins related with disease was proposed as one of the solutions from the problem of experimental method application which is generally high cost and time consuming. The case of study was conducted on Diabetes Melitus (DM) type 2 diseases. Identification of significant proteins was conducted by constructing protein-protein interactions network and then analyzing the network topology. Dataset was obtained from Online Mendelian Inheritance in Man (OMIM) and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) which provided protein data related with a disease and Protein-Protein Interaction (PPI), respectively. The results of topology analysis towards Protein-Protein Interaction (PPI) showed that there were 21 significant protein associated with DM where INS as a network center protein and AKTI, TCF7L2, KCNJ11, PPARG, GCG, INSR, IAPP, SOCS3 were proteins that had close relation directly with INS.