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A Novel Three-Stage Filter-Wrapper Framework for miRNA Subset Selection in Cancer Classification

机译:癌症分类中的miRNA子集选择的一种新型三阶段滤波器包装框架

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摘要

Micro-Ribonucleic Acids (miRNAs) are small non-coding Ribonucleic Acid (RNA) molecules that play an important role in the cancer growth. There are a lot of miRNAs in the human body and not all of them are responsible for cancer growth. Therefore, there is a need to propose the novel miRNA subset selection algorithms to remove irrelevant and redundant miRNAs and find miRNAs responsible for cancer development. This paper tries to propose a novel three-stage miRNAs subset selection framework for increasing the cancer classification accuracy. In the first stage, multiple filter algorithms are used for ranking the miRNAs according to their relevance with the class label, and then generating a miRNA pool obtained based on the top-ranked miRNAs of each filter algorithm. In the second stage, we first rank the miRNAs of the miRNA pool by multiple filter algorithms and then this ranking is used to weight the probability of selecting each miRNA. In the third stage, Competitive Swarm Optimization (CSO) tries to find an optimal subset from the weighed miRNAs of the miRNA pool, which give us the most information about the cancer patients. It should be noted that the balance between exploration and exploitation in the proposed algorithm is accomplished by a zero-order Fuzzy Inference System (FIS). Experiments on several miRNA cancer datasets indicate that the proposed three-stage framework has a great performance in terms of both the low error rate of the cancer classification and minimizing the number of miRNAs.
机译:微核糖核酸(miRNA)是在癌症生长中发挥重要作用的小非编码核糖核酸(RNA)分子。人体中有很多miRNA,并非所有的癌症都对癌症生长负责。因此,需要提出新的miRNA子集选择算法以去除无关和冗余的miRNA,并找到负责癌症发展的miRNA。本文试图提出一种新型三级MiRNA子集选择框架,用于增加癌症分类准确性。在第一阶段中,多个过滤算法用于根据其与类标签的相关性对MIRNA进行排序,然后生成基于每个滤波器算法的顶级MIRNA获得的miRNA池。在第二阶段,我们首先通过多个过滤算法排名miRNA池的miRNA,然后该排名用于重量选择每个miRNA的概率。在第三阶段,竞争群优化(CSO)试图找到MiRNA池的称重MiRNA的最佳子集,这给了我们最多的关于癌症患者的信息。应当注意,通过零阶模糊推理系统(FIS)实现了所提出的算法中的勘探和开发之间的平衡。在几种miRNA癌数据集上的实验表明,所提出的三阶段框架在癌症分类的低错误率和最小化miRNA的数量方面具有很大的性能。

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