MicroRNA （miRNA） plays vital roles in biological processes like RNA splicing and regulation of gene expression. Studies have revealed that there might be possible links between onco- genesis and expression profiles ...MicroRNA （miRNA） plays vital roles in biological processes like RNA splicing and regulation of gene expression. Studies have revealed that there might be possible links between onco- genesis and expression profiles of some miRNAs, due to their differential expression between normal and tumor tissues. However, the automatic classification of miRNAs into different cate- gories by considering the similarity of their expression values has rarely been addressed. This article proposes a solution framework for solving some real-life classification problems related to cancer, miRNA, and mRNA expression datasets. In the first stage, a mulfiobjective optimization based framework, non-dominated sorting genetic algorithm II, is proposed to automatically determine the appropriate classifier type, along with its suitable parameter and feature combinations, pertinent for classifying a given dataset. In the second page, a stack-based ensemble technique is employed to get a single combinatorial solution from the set of solutions obtained in tke first stage. The performance of the proposed two-stage approach is evaluated on several cancer and RNA expression pro- file datasets. Compared to several state-of-the-art approaches for classifying different datasets, our method shows supremacy in the accuracy of classification.展开更多
文摘MicroRNA （miRNA） plays vital roles in biological processes like RNA splicing and regulation of gene expression. Studies have revealed that there might be possible links between onco- genesis and expression profiles of some miRNAs, due to their differential expression between normal and tumor tissues. However, the automatic classification of miRNAs into different cate- gories by considering the similarity of their expression values has rarely been addressed. This article proposes a solution framework for solving some real-life classification problems related to cancer, miRNA, and mRNA expression datasets. In the first stage, a mulfiobjective optimization based framework, non-dominated sorting genetic algorithm II, is proposed to automatically determine the appropriate classifier type, along with its suitable parameter and feature combinations, pertinent for classifying a given dataset. In the second page, a stack-based ensemble technique is employed to get a single combinatorial solution from the set of solutions obtained in tke first stage. The performance of the proposed two-stage approach is evaluated on several cancer and RNA expression pro- file datasets. Compared to several state-of-the-art approaches for classifying different datasets, our method shows supremacy in the accuracy of classification.
