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

机译:应用模糊C均值和拓扑分析识别2型重要蛋白糖尿病

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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.
机译:解决实验方法应用问题的解决方法之一是提出一种确定与某种疾病有关的蛋白质的重要​​性的计算方法,该方法通常是费时费力的。该研究案例是针对2型糖尿病(DM)疾病进行的。这项研究的目的是通过应用模糊C均值聚类算法和图论的拓扑分析来识别导致2型糖尿病的重要蛋白质。总共获得了100种蛋白质,其中一些被鉴定为最重要的蛋白质,例如GCK,HNF4A,SLC30A8,SLC2A2,NEUROD1,PPARG,IRS1,HNF1B,PDX1和RETN。预期该结果可被药理研究人员用来筛选与代表2型糖尿病(DM)疾病的蛋白质相关的活性化合物的候选物。

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