文章基本信息

标题：A Novel Three-Stage Filter-Wrapper Framework for miRNA Subset Selection in Cancer Classification
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作者：Mohammad Bagher Dowlatshahi ; Vali Derhami ; Hossein Nezamabadi-pour 等
期刊名称：Informatics
电子版ISSN：2227-9709
出版年度：2018
卷号：5
期号：1
页码：13-31
DOI：10.3390/informatics5010013
出版社：MDPI Publishing
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.
关键词：miRNA subset selection; hybrid filter-wrapper; three-stage framework; high-dimensionality; Competitive Swarm Optimization; cancer classification miRNA subset selection ; hybrid filter-wrapper ; three-stage framework ; high-dimensionality ; Competitive Swarm Optimization ; cancer classification