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Selection of interdependent genes via dynamic relevance analysis for cancer diagnosis

机译:通过动态相关分析选择相互依赖的基因进行癌症诊断

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Microarray analysis is widely accepted for human cancer diagnosis and classification. However the high dimensionality of microarray data poses a great challenge to classification. Gene selection plays a key role in identifying salient genes from thousands of genes in microarray data that can directly contribute to the symptom of disease. Although various excellent selection methods are currently available, one common problem of these methods is that genes which have strong discriminatory power as a group but are weak as individuals will be discarded. In this paper, a new gene selection method is proposed for cancer diagnosis and classification by retaining useful intrinsic groups of interdependent genes. The primary characteristic of this method is that the relevance between each gene and target will be dynamically updated when a new gene is selected. The effectiveness of our method is validated by experiments on six publicly available microarray data sets. Experimental results show that the classification performance and enrichment score achieved by our proposed method is better than those of other selection methods.
机译:微阵列分析已被广泛接受用于人类癌症的诊断和分类。然而,微阵列数据的高维数对分类提出了很大的挑战。基因选择在从微阵列数据中的数千个基因中识别出显着基因中起着关键作用,这些基因可以直接导致疾病的症状。尽管目前有各种优良的选择方法,但是这些方法的一个普遍问题是将具有强大的整体鉴别能力但由于个体而较弱的基因丢弃。在本文中,通过保留有用的相互依赖基因的内在基团,提出了一种用于癌症诊断和分类的新基因选择方法。该方法的主要特点是,当选择一个新基因时,每个基因与靶标之间的相关性将动态更新。通过对六个公开可用的微阵列数据集进行的实验验证了我们方法的有效性。实验结果表明,本文提出的方法实现的分类性能和富集得分优于其他选择方法。

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