Ovarian cancer is the deadliest gynecologic malignancy, mainly due to limitations in early diagnosis. With advances in high-throughput technologies, research interest in identifying novel and customized tumor biomarkers for early detection and diagnosis is rapidly growing. Here we introduce a new tool called EBST to select microRNAs with biomarker potency in ovarian cancer. This tool has pre-processing options and Its core is the use of Modified Multi Objective Imperialist Competitive Algorithm and six objective functions based on the classifier performance/structure evaluation, clustering error and mRMR filter. In this paper, we used the FDR filter in the pre-processing stage and considered five objective functions, four of which relate to the l ₁ -SVM classifier performance and one to the average mRMR ranking. The proposed method has identified 11 microRNAs including hsa-miR-6784-5p, hsa-miR-1228-5p, hsa-miR-8073, hsa-miR-6756-5p, hsa-miR-1307-3p, hsa-miR-4697-5p, hsa-miR-3663-3p, hsa-miR-328-5p, hsa-miR-1228-3p, hsa-miR-6821-5p, hsa-miR-1268a . Data classification by the proposed model showed 100 percent sensitivity, 99.38 percent specificity, 99.69 percent accuracy and 99.39 percent positive predictive value. In comparison with routine state-of-the-art methods, superiority of our method was confirmed. The biological evaluation of selected microRNAs using bioinformatics tools and published articles confirms their role in cancer signaling pathways. The tool and its MATLAB code are freely available at https://github.com/hanif-y .

中文翻译：

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EBST：一种基于进化多目标优化的工具，用于发现卵巢癌中的潜在生物标志物

卵巢癌是最致命的妇科恶性肿瘤，主要是由于早期诊断的局限性。随着高通量技术的进步，识别用于早期检测和诊断的新型和定制的肿瘤生物标志物的研究兴趣正在迅速增长。在这里，我们介绍了一种名为 EBST 的新工具，用于选择在卵巢癌中具有生物标志物效力的 microRNA。该工具具有预处理选项，其核心是使用改进的多目标帝国主义竞争算法和基于分类器性能/结构评估、聚类误差和 mRMR 滤波器的六个目标函数。在本文中，我们在预处理阶段使用了 FDR 滤波器，并考虑了五个目标函数，其中四个与l ₁ -SVM分类器性能和平均mRMR排名。所提出的方法已鉴定出 11 个 microRNA，包括hsa-miR-6784-5p、hsa-miR-1228-5p、hsa-miR-8073、hsa-miR-6756-5p、hsa-miR-1307-3p、hsa-miR-4697-5p、hsa-miR- 3663-3p、hsa-miR-328-5p、hsa-miR-1228-3p、hsa-miR-6821-5p、hsa-miR-1268a。所提出模型的数据分类显示出 100% 的敏感性、99.38% 的特异性、99.69% 的准确度和 99.39% 的阳性预测值。与常规的最先进方法相比，我们的方法的优越性得到了证实。使用生物信息学工具和发表的文章对选定的 microRNA 进行生物学评估，证实了它们在癌症信号通路中的作用。该工具及其 MATLAB 代码可在以下位置免费获得https://github.com/hanif-y .