Discovering Cancer-Related miRNAs from miRNA-Target Interactions by Support Vector Machines
Publication in refereed journal

替代計量分析
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其它資訊
摘要MicroRNAs (miRNAs) have been shown to be closely related to cancer progression. Traditional methods for discovering cancer-related miRNAs mostly require significant marginal differential expression, but some cancer-related miRNAs may be non-differentially or only weakly differentially expressed. Such miRNAs are called dark matters miRNAs (DM-miRNAs) and are targeted through the Pearson correlation change on miRNA-target interactions (MTIs), but the efficiency of their method heavily relies on restrictive assumptions. In this paper, a novel method was developed to discover DM-miRNAs using support vector machine (SVM) based on not only the miRNA expression data but also the expression of its regulating target. The application of the new method in breast and kidney cancer datasets found, respectively, 9 and 24 potential DM-miRNAs that cannot be detected by previous methods. Eight and 15 of the newly discovered miRNAs have been found to be associated with breast and kidney cancers, respectively, in existing literature. These results indicate that our new method is more effective in discovering cancer-related miRNAs.
出版社接受日期14.01.2020
著者Pian C, Mao SJ, Zhang GL, Du J, Li F, Leung SY, Fan XD
期刊名稱Molecular Therapy - Nucleic Acids
出版年份2020
月份3
卷號19
出版社Elsevier (Cell Press): OAJ / Elsevier
頁次1423 - 1433
國際標準期刊號2162-2531
語言英式英語
Web of Science 學科類別Medicine, Research & Experimental;Research & Experimental Medicine

上次更新時間 2020-02-12 於 23:42