Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India.
Int J Nanomedicine. 2013;8 Suppl 1(Suppl 1):63-74. doi: 10.2147/IJN.S40739. Epub 2013 Sep 16.
The microRNAs, also known as miRNAs, are the class of small noncoding RNAs. They repress the expression of a gene posttranscriptionally. In effect, they regulate expression of a gene or protein. It has been observed that they play an important role in various cellular processes and thus help in carrying out normal functioning of a cell. However, dysregulation of miRNAs is found to be a major cause of a disease. Various studies have also shown the role of miRNAs in cancer and the utility of miRNAs for the diagnosis of cancer and other diseases. Unlike with mRNAs, a modest number of miRNAs might be sufficient to classify human cancers. However, the absence of a robust method to identify differentially expressed miRNAs makes this an open problem. In this regard, this paper presents a novel approach for in silico identification of differentially expressed miRNAs from microarray expression data sets. It integrates judiciously the theory of rough sets and merit of the so-called B.632+ bootstrap error estimate. While rough sets select relevant and significant miRNAs from expression data, the B.632+ error rate minimizes the variability and bias of the derived results. The effectiveness of the proposed approach, along with a comparison with other related approaches, is demonstrated on several miRNA microarray expression data sets, using the support vector machine.
微 RNA（miRNA）也被称为 miRNAs，是一类小的非编码 RNA。它们在后转录水平上抑制基因的表达。实际上，它们调节基因或蛋白质的表达。已经观察到它们在各种细胞过程中发挥重要作用，从而有助于细胞的正常功能。然而，miRNA 的失调被发现是疾病的一个主要原因。各种研究还表明了 miRNAs 在癌症中的作用，以及 miRNAs 在癌症和其他疾病的诊断中的应用。与 mRNAs 不同，少量的 miRNAs 可能足以对人类癌症进行分类。然而，缺乏一种强大的方法来识别差异表达的 miRNAs 使得这个问题仍然存在。在这方面，本文提出了一种从微阵列表达数据集识别差异表达 miRNAs 的新方法。它明智地集成了粗糙集理论和所谓的 B.632+引导误差估计的优点。虽然粗糙集从表达数据中选择相关和重要的 miRNAs，但 B.632+错误率最小化了得出结果的可变性和偏差。使用支持向量机，在几个 miRNA 微阵列表达数据集上对所提出的方法的有效性进行了验证，并与其他相关方法进行了比较。
