Mousavi Reza, Eftekhari Mahdi, Haghighi Mehdi Ghezelbash
* Department of Electrical and Computer Engineering, Graduate University of Advanced Technology, Kerman, Iran.
† Department of Computer Engineering, Shahid Bahonar University of Kerman, Iran.
J Bioinform Comput Biol. 2015 Dec;13(6):1550017. doi: 10.1142/S0219720015500171. Epub 2015 May 28.
MicroRNAs (miRNAs) are small non-coding RNAs that have important functions in gene regulation. Since finding miRNA target experimentally is costly and needs spending much time, the use of machine learning methods is a growing research area for miRNA target prediction. In this paper, a new approach is proposed by using two popular ensemble strategies, i.e. Ensemble Pruning and Rotation Forest (EP-RTF), to predict human miRNA target. For EP, the approach utilizes Genetic Algorithm (GA). In other words, a subset of classifiers from the heterogeneous ensemble is first selected by GA. Next, the selected classifiers are trained based on the RTF method and then are combined using weighted majority voting. In addition to seeking a better subset of classifiers, the parameter of RTF is also optimized by GA. Findings of the present study confirm that the newly developed EP-RTF outperforms (in terms of classification accuracy, sensitivity, and specificity) the previously applied methods over four datasets in the field of human miRNA target. Diversity-error diagrams reveal that the proposed ensemble approach constructs individual classifiers which are more accurate and usually diverse than the other ensemble approaches. Given these experimental results, we highly recommend EP-RTF for improving the performance of miRNA target prediction.
微小RNA（miRNA）是一类在基因调控中具有重要功能的小型非编码RNA。由于通过实验寻找miRNA靶标成本高昂且耗时，因此使用机器学习方法进行miRNA靶标预测是一个不断发展的研究领域。本文提出了一种新方法，通过使用两种流行的集成策略，即集成剪枝和旋转森林（EP-RTF），来预测人类miRNA靶标。对于EP，该方法利用遗传算法（GA）。换句话说，首先通过GA从异构集成中选择一个分类器子集。接下来，基于RTF方法对所选分类器进行训练，然后使用加权多数投票进行组合。除了寻找更好的分类器子集外，RTF的参数也通过GA进行优化。本研究结果证实，新开发的EP-RTF在人类miRNA靶标领域的四个数据集中，在分类准确率、灵敏度和特异性方面均优于先前应用的方法。多样性-误差图表明，所提出的集成方法构建的个体分类器比其他集成方法更准确且通常更具多样性。鉴于这些实验结果，我们强烈推荐EP-RTF用于提高miRNA靶标预测的性能。
