MicroRNAs (miRNAs) are a kind of small noncoding RNA molecules that are direct posttranscriptional regulations of mRNA targets. Studies have indicated that miRNAs play key roles in complex diseases by taking part in many biological processes, such as cell growth, cell death and so on. Therefore, in order to improve the effectiveness of disease diagnosis and treatment, it is appealing to develop advanced computational methods for predicting the essentiality of miRNAs. In this study, we propose a method (PESM) to predict the miRNA essentiality based on gradient boosting machines and miRNA sequences. First, PESM extracts the sequence and structural features of miRNAs. Then it uses gradient boosting machines to predict the essentiality of miRNAs. We conduct the 5-fold cross-validation to assess the prediction performance of our method. The area under the receiver operating characteristic curve (AUC), F-measure and accuracy (ACC) are used as the metrics to evaluate the prediction performance. We also compare PESM with other three competing methods which include miES, Gaussian Naive Bayes and Support Vector Machine. The results of experiments show that PESM achieves the better prediction performance (AUC: 0.9117, F-measure: 0.8572, ACC: 0.8516) than other three computing methods. In addition, the relative importance of all features also further shows that newly added features can be helpful to improve the prediction performance of methods.

中文翻译：

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PESM：基于梯度增强机器和序列预测miRNA的必要性。

微小RNA（miRNA）是一种小的非编码RNA分子，是mRNA靶标的直接转录后调控。研究表明，miRNA通过参与许多生物过程（例如细胞生长，细胞死亡等）而在复杂疾病中发挥关键作用。因此，为了提高疾病诊断和治疗的有效性，吸引人的是开发用于预测miRNA的本质的先进的计算方法。在这项研究中，我们提出了一种基于梯度增强机器和miRNA序列预测miRNA必要性的方法（PESM）。首先，PESM提取miRNA的序列和结构特征。然后，它使用梯度增强机器来预测miRNA的必要性。我们进行5倍交叉验证，以评估我们方法的预测性能。接收器工作特性曲线（AUC），F量度和准确性（ACC）下的面积用作评估预测性能的指标。我们还将PESM与其他三种竞争方法进行了比较，包括miES，高斯朴素贝叶斯和支持向量机。实验结果表明，与其他三种计算方法相比，PESM具有更好的预测性能（AUC：0.9117，F-measure：0.8572，ACC：0.8516）。此外，所有特征的相对重要性还进一步表明，新添加的特征可能有助于改善方法的预测性能。高斯朴素贝叶斯和支持向量机。实验结果表明，与其他三种计算方法相比，PESM具有更好的预测性能（AUC：0.9117，F-measure：0.8572，ACC：0.8516）。此外，所有特征的相对重要性还进一步表明，新添加的特征可能有助于改善方法的预测性能。高斯朴素贝叶斯和支持向量机。实验结果表明，与其他三种计算方法相比，PESM具有更好的预测性能（AUC：0.9117，F-measure：0.8572，ACC：0.8516）。此外，所有特征的相对重要性还进一步表明，新添加的特征可能有助于改善方法的预测性能。