Hunan Provincial Key Lab on Bioinformtics, School of Computer Science and Engineering, Central South University, 932 South Lushan Rd, ChangSha, 410083, China.
School of Computer and Information,Qiannan Normal University for Nationalities, Longshan Road, DuYun, 558000, China.
BMC Bioinformatics. 2020 Mar 18;21(1):111. doi: 10.1186/s12859-020-3426-9.
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.
MicroRNAs（miRNAs）是一种小的非编码 RNA 分子，可直接对 mRNA 靶标进行转录后调控。研究表明，miRNAs 通过参与细胞生长、细胞死亡等许多生物过程，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效果，开发用于预测 miRNA 必需性的先进计算方法是很有吸引力的。
在本研究中，我们提出了一种基于梯度提升机和 miRNA 序列的 miRNA 必需性预测方法（PESM）。首先，PESM 提取 miRNA 的序列和结构特征。然后，它使用梯度提升机来预测 miRNA 的必需性。我们进行了 5 折交叉验证来评估我们方法的预测性能。接收者操作特征曲线（ROC）下的面积（AUC）、F 度量和准确性（ACC）被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（miES、高斯朴素贝叶斯和支持向量机）进行了比较。
实验结果表明，PESM 比其他三种计算方法具有更好的预测性能（AUC：0.9117，F 度量：0.8572，ACC：0.8516）。此外，所有特征的相对重要性也进一步表明，新添加的特征有助于提高方法的预测性能。
