MultiMiTar: a novel multi objective optimization based miRNA-target prediction method.
MultiMiTar: a novel multi objective optimization based miRNA-target prediction method.
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多学院：一种新型的基于多物镜优化的基于miRNA目标预测方法。
DOI：
10.1371/journal.pone.0024583
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Bandyopadhyay S
中科院分区：
文献类型：
--
作者：
Mitra R;Bandyopadhyay S
基于机器学习的 miRNA 目标预测算法通常无法在灵敏度和特异性方面获得平衡的预测精度，因为缺乏负例的黄金标准、miRNA 目标位点上下文特定的相关特征和高效的特征选择过程。而且，所有基于序列、结构和机器学习的算法都无法将真阳性预测优先分配在排名列表的顶部；因此，这些算法对于生物学家来说变得不可靠。此外，这些算法无法获得在蛋白质水平上翻译抑制的目标转录本的精确度和召回率的相当大的组合。在本文中，我们介绍了一种高效的 miRNA 目标预测系统 MultiMiTar，这是一种基于支持向量机 (SVM) 的分类器，集成了基于多目标元启发式的特征选择技术。该方法的稳健性能主要归功于使用高质量的反例和选择生物学相关的 miRNA 靶向位点上下文特定特征。通过使用新颖的特征选择技术 AMOSA-SVM 来选择特征，该技术集成了多目标优化技术存档多目标模拟退火 (AMOSA) 和 SVM。在完全独立的测试数据集上，与其他目标预测方法相比，MultiMiTar 的马修相关系数 (MCC) 达到了 0.583，平均分类准确度 (ACA) 达到了 0.8。这些算法获得的MCC和ACA值范围分别为-0.269至0.155和0.321至0.582。此外，与所有其他现有方法相比，它在平移抑制数据集的精度和灵敏度（召回率）方面显示出更平衡的结果。一个重要的方面是，真正的阳性预测优先分布在排名列表的顶部，这使得 MultiMiTar 对于生物学家来说是可靠的。 MultiMiTar 现已作为在线工具提供，网址为 www.isical.ac.in/~bioinfo_miu/multimitar.htm。 MultiMiTar 软件可以从 www.isical.ac.in/~bioinfo_miu/multimitar-download.htm 下载。
Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level. In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM. MultiMiTar is found to achieve much higher Matthew’s correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from −0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. MultiMiTar is now available as an online tool at www.isical.ac.in/~bioinfo_miu/multimitar.htm. MultiMiTar software can be downloaded from www.isical.ac.in/~bioinfo_miu/multimitar-download.htm.
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