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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >A FLEXIBLE NONPARAMETRIC APPROACH TO FIND CANDIDATE GENES ASSOCIATED WITH DISEASE IN MICROARRAY EXPERIMENTS
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A FLEXIBLE NONPARAMETRIC APPROACH TO FIND CANDIDATE GENES ASSOCIATED WITH DISEASE IN MICROARRAY EXPERIMENTS

机译：在微阵列实验中寻找与疾病相关的候选基因的灵活的非参数方法

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摘要

Very often biologists are interested to know the biological function of a particular gene. Its true biological function may depend on other genes. Finding other genes in the same biological pathway of that gene may enhance further understanding of its biological function. Therefore, we are interested in finding other candidate genes whose expression values are highly correlated with that of a "seed" gene. The "seed" gene, which is known and associated with a disease, is used as a reference to extract candidate genes from microarray experiments and enriched pathways. We propose a nonparametric procedure for selecting the candidate genes. The expression levels for these candidate genes are correlated with that of a "seed" gene in microarray experiments. The proposed test statistic compares two Area Under Receiver Operating Characteristic Curves (AUC) for gene pairs, taking implicit correlation between two AUCs into account. The performance of our method is compared to the other well-known methods through the use of simulation and real data analysis.
机译：生物学家经常有兴趣了解特定基因的生物学功能。它的真正生物学功能可能取决于其他基因。在该基因的同一生物学途径中寻找其他基因可能会增强对其生物学功能的进一步了解。因此，我们有兴趣寻找其表达值与“种子”基因高度相关的其他候选基因。已知并与疾病相关的“种子”基因被用作从微阵列实验和富集途径中提取候选基因的参考。我们提出了一种非参数程序来选择候选基因。在微阵列实验中，这些候选基因的表达水平与“种子”基因的表达水平相关。拟议的测试统计数据比较了两个成对的接收器工作区特征曲线下的两个区域，并考虑了两个AUC之间的隐式相关性。通过使用模拟和真实数据分析，将我们的方法的性能与其他知名方法进行了比较。

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