Discovery of MicroRNA markers: An SVM-based multiobjective feature selection approach

A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay
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引用次数: 5

Abstract

MicroRNAs (miRNAs) are small non-coding RNAs that have been shown to play important roles in gene regulation and various biological processes. The abnormal expression of some specific miRNAs often results in the development of cancer. In this article, we have utilized a multiobjective genetic algorithm-based feature selection algorithm wrapped with support vector machine (SVM) classifier for selecting promising miRNAs having differential expression in benign and malignant tissue samples. Subsequently, the non-dominated sets of promising miRNAs are aggregated into a single most promising miRNA subset. Finally, the Signal-to-Noise Ratio (SNR) statistic has been applied on the obtained miRNA subset for identifying potential miRNA markers that distinguish the two classes (benign and malignant) of tissue samples. The performance has been demonstrated on four real-life miRNA expression datasets for different SVM kernel functions and the identified miRNA markers are reported.
MicroRNA标记物的发现:基于svm的多目标特征选择方法
MicroRNAs (miRNAs)是一种小的非编码rna,在基因调控和各种生物过程中发挥着重要作用。一些特异性mirna的异常表达往往导致癌症的发生。在本文中,我们使用了一种基于多目标遗传算法的特征选择算法,该算法与支持向量机(SVM)分类器包裹在一起,用于选择在良性和恶性组织样本中具有差异表达的有希望的mirna。随后,非显性的有希望的miRNA集合被聚集成一个最有希望的miRNA子集。最后,将信噪比(SNR)统计量应用于获得的miRNA子集,以识别区分两类(良性和恶性)组织样本的潜在miRNA标记。该性能已在四个实际的miRNA表达数据集上进行了验证,用于不同的支持向量机核函数,并报道了识别的miRNA标记。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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