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Integrated analysis of gene expression and genome-wide DNA methylation for tumor prediction: An association rule mining-based approach

机译：基因表达和全基因组DNA甲基化的综合分析用于肿瘤预测：基于关联规则挖掘的方法

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摘要

Statistical analysis and association rule mining are two most efficient techniques, where the first one is used to identify differentially expressed/methylated genes across different types of samples or experimental conditions and the second one is used to determine expression/methylation relationships among them. In this article, we have performed an integrated analysis of statistical methods and association rule mining on mRNA expression and DNA methylation datasets for the prediction of Uterine Leiomyoma. Moreover, we have proposed a novel rule-base classifier. Depending on 16 different rule-interestingness measures, we have applied a Genetic Algorithm based rank aggregation technique on the association rules which are generated from the training data by Apriori association rule mining algorithm. After determining the ranks of the rules, we have conducted a majority voting technique on each test point to determine its class-label (i.e. tumor or normal class-label) through weighted-sum method. We have run this classifier on the combined dataset using k-fold cross-validation and also performed a comparative performance analysis with other popular rule-base classifiers. Finally, we have predicted the status of some important genes (through frequency analysis in association rules for tumor and normal class-labels individually) that have a major role for tumor formation in Uterine Leiomyoma.
机译：统计分析和关联规则挖掘是两种最有效的技术，其中第一种用于跨不同类型的样品或实验条件识别差异表达/甲基化的基因，第二种用于确定它们之间的表达/甲基化关系。在本文中，我们对mRNA的表达和DNA甲基化数据集进行了统计方法和关联规则挖掘的综合分析，以预测子宫平滑肌瘤。此外，我们提出了一种新颖的基于规则的分类器。根据16种不同的规则兴趣度量，我们对基于Apriori关联规则挖掘算法从训练数据生成的关联规则应用了基于遗传算法的秩聚合技术。在确定规则等级之后，我们对每个测试点进行了多数表决技术，以加权和方法确定其类别标签（即肿瘤或正常类别标签）。我们已经使用k折交叉验证对组合数据集运行了该分类器，并且还与其他流行的基于规则的分类器进行了比较性能分析。最后，我们已经预测了一些重要基因的状态（通过频率分析，分别针对肿瘤和正常类别标签的关联规则），这些基因对子宫平滑肌瘤的肿瘤形成具有重要作用。

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