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 as Dark Matters miRNAs (DM-miRNAs) and targeted by Pian et al. (2018) 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. 8 and 15 of the newly discovered miRNAs have been found to be associated with breast and kidney cancers respectively in existing literature, respectively. These results indicate that our new method is more effective in discovering cancer-related miRNAs.

miRNA已显示与癌症进展密切相关。发现癌症相关miRNA的传统方法大多需要显着的边缘差异表达，但某些癌症相关miRNA可能是非差异表达或仅弱差异表达。此类miRNA被称为Dark Matters miRNA（DM-miRNA），并被Pian等人靶向。（2018）通过针对miRNA-靶标相互作用（MTI）的Pearson相关性变化; 但是他们方法的效率在很大程度上取决于限制性假设。在本文中，基于miRNA表达数据及其调控靶点的表达，开发了一种使用支持​​向量机（SVM）发现DM-miRNA的新方法。新方法在乳腺癌和肾癌数据集中的应用分别发现了9种和24种潜在的DM-miRNA，这些都是以前的方法无法检测到的。在现有文献中，分别发现分别有8种和15种新发现的miRNA与乳腺癌和肾癌有关。这些结果表明我们的新方法在发现癌症相关的miRNA方面更有效。