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Predictive Analytics to Determine the Potential Occurrence of Genetic Disease and their Correlation: Osteoporosis and Cardiovascular Disease

机译:预测分析确定遗传疾病的潜在发生及其相关性:骨质疏松症和心血管疾病

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In this paper, a Predictive Analytics Model is designed, developed, and validated to determine the risk of manifesting osteoporosis in later life using big data processing. The proposed model leverages the novel genetic pleiotropic information in the 1,000 Genome Project of over 2,500 individuals world-wide. Also, the mutations associated with osteoporosis and cardiovascular disease are specifically analyzed. The study proposes the automatic histogram clustering as an effective and intuitive visualization method for high dimensional dataset. The results demonstrate a significant correlation between a person's regional background and the frequency of occurrence of the 35 Single Nucleotide Polymorphisms (SNPs) associated with osteoporosis and/or cardiovascular disease (CVD). Machine learning algorithms, such as Logistic Regression, Adaboost, and KNN are then applied to predict the occurrence of 7 osteoporosis-related-SNPs based on the existing CVD-relatcd-SNPs input. Finally, the developed model is evaluated using a separate dataset obtained through Affymetrix microarray mRNA expression signal values for the specific SNP(s) in individuals with and without osteoporosis.
机译:在本文中,设计了一种预测分析模型,开发,验证,以确定使用大数据处理在后期植物中表现出骨质疏松症的风险。拟议的模型利用了全世界超过2,500个以上的1,000个基因组项目的新型遗传型血频信息。而且,特别分析了与骨质疏松症和心血管疾病相关的突变。该研究提出了自动直方图聚类作为高维数据集的有效和直观可视化方法。结果表明,人的区域背景与35个与骨质疏松症和/或心血管疾病(CVD)相关的35个单核苷酸多态性(SNP)的发生频率之间的显着相关性。然后应用机器学习算法,例如逻辑回归,adaboost和kNN,以预测基于现有的CVD-Relatcd-SNP输入的7个骨质疏松症相关SNP的发生。最后,使用通过在具有和不具有骨质疏松症的个体中的特定SNP(s)获得的单独数据集来评估开发模型。

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