EBST: An Evolutionary Multi-Objective Optimization Based Tool for Discovering Potential Biomarkers in Ovarian Cancer
EBST: An Evolutionary Multi-Objective Optimization Based Tool for Discovering Potential Biomarkers in Ovarian Cancer
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DOI：
10.1109/tcbb.2020.2993150
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发表时间：
2020-05
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影响因子：
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通讯作者：
Hanif Yaghoobi;E. Babaei;B. Hussen;Ali Emami
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文献类型：
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作者：
Hanif Yaghoobi;E. Babaei;B. Hussen;Ali Emami
卵巢癌是最致命的妇科恶性肿瘤，主要是由于早期诊断的限制。随着高通量技术的进步，识别新的和定制的肿瘤生物标志物用于早期检测和诊断的研究兴趣正在迅速增长。在这里，我们介绍了一种名为EBST的新工具来选择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l1-SVM分类器性能有关，一个与平均mRMR排名有关。该方法鉴定了11种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。该模型的数据分类灵敏度为100%，特异度为99.38%，准确率为99.69%，阳性预测值为99.39%。通过与常规方法的比较，证实了该方法的优越性。使用生物信息学工具和发表的文章对选定的microrna进行生物学评价，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https://github.com/hanif-y免费获得。
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 l1-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.