Identification of Significant Protein Diabetes Mellitus Type 2 with Fuzzy C- Means and Topological Analysis

Computational approach for identifying significance of proteins related to a certain disease was proposed as one of the solutions from the problem of experimental method application which is generally cost and time consuming. The case of study was conducted on diabetes mellitus (DM) type 2 disease. The purpose of this research is to identify significant proteins that causes diabetes mellitus type 2 by applying Fuzzy C-Means clustering algorithm and topological analysis from graph theory. A total of 100 proteins were obtained, some of them were identified as most significant proteins such as GCK, HNF4A, SLC30A8, SLC2A2, NEUROD1, PPARG, IRS1, HNF1B, PDX1 and RETN. It is expected that this results can be used by pharmacology researcher to screen the candidates of active compounds that have association with those proteins that representing diabetes mellitus (DM) type 2 disease.

[1]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[2]  Reinhard Schneider,et al.  Which clustering algorithm is better for predicting protein complexes? , 2011, BMC Research Notes.

[3]  Aidong Zhang,et al.  Clustering Methods in a Protein–Protein Interaction Network , 2007 .

[4]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[5]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[6]  Gan Siew Lee,et al.  Network topology of Indonesian stock market , 2012, 2012 International Conference on Cloud Computing and Social Networking (ICCCSN).

[7]  Damian Szklarczyk,et al.  The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible , 2016, Nucleic Acids Res..

[8]  Ney Lemke,et al.  Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information , 2009, BMC Bioinformatics.

[9]  J. Callés-Escandon,et al.  Diabetes and endothelial dysfunction: a clinical perspective. , 2001, Endocrine reviews.

[10]  Xiaoyan Zhu,et al.  Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts , 2009, PLoS Comput. Biol..

[11]  The Uniprot Consortium.  UniProt: the universal protein knowledgebase , 2018, Nucleic acids research.

[12]  C. Jeffery.  New Ideas on Protein Moonlighting , 2013 .

[13]  Anil Kumar Gupta,et al.  A Comparative study Between Fuzzy Clustering Algorithm and Hard Clustering Algorithm , 2014, ArXiv.