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Classification of Gastric Cancer Subtypes using ICA, MLR and Bayesian Network

机译:使用ICA,MLR和Bayesian网络分类胃癌亚型

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The purpose of this study is to extract genes that could distinguish between two subtypes (diffuse and intestinal) of gastric cancer (GC) from their gene expression profile, to build a classifier based on the genes and to investigate the relationships among the selected genes, because there are big differences in the survival curve and the medical treatment between the subtypes. First, we applied Wilcoxon test, independent component analysis (ICA) and multiple logistic regression (MLR) to the profile, and conducted a classifier consisting of only three genes. We validated the classifier using test data with new 9 samples (blind test). The classifier yielded the accuracy of 100%. Finally, using the 18 genes selected by MLR and specific to each subtype, Bayesian Network was constructed, and compared with the network from GeneMANIA. Consequently, these networks were very similar each other. ^g>Independent Component Analysis, Multiple Logistic Regression Analysis, Pathway Analysis, Bayesian Network
机译:本研究的目的是提取可以区分胃癌(GC)的两种亚型(扩散和肠)的基因,以基于基因构建分类器,并研究所选基因之间的关系,因为生存曲线存在巨大差异和亚型之间的医疗。首先,我们将Wilcoxon测试,独立分析(ICA)和多个逻辑回归(MLR)应用于轮廓,并进行了仅由三个基因组成的分类器。我们验证使用新的9个样品(盲测)测试数据的分类。分类器产生100%的准确性。最后,使用由MLR选择的18个基因并且特定于每个亚型,构建贝叶斯网络,并与来自Genemania的网络进行比较。因此,这些网络彼此非常相似。 ^ G>独立分量分析,多重逻辑回归分析,途径分析,贝叶斯网络

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