• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2nd international conference on bioinformatics and computational biology 2010 >CLUSTERING MICRO-RNA ARRAY DATA USING AN INFORMATION FUSION-BASED APPROACH WITH MULTIPLE TYPES OF INPUT DATA
【24h】

CLUSTERING MICRO-RNA ARRAY DATA USING AN INFORMATION FUSION-BASED APPROACH WITH MULTIPLE TYPES OF INPUT DATA

机译：使用基于信息融合的多种输入数据方法聚类微RNA阵列数据

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

MicroRNAs (miRNAs) are small non-coding molecules that have been shown to play key roles in regulating cellular development and to be involved in various diseases. By interfering with their target mRNAs, these molecules inhibit the expression of proteins, either by destabilizing the mRNA molecule or by preventing its translation. It has been suggested that each miRNA can target hundreds of mRNAs, and that one mRNA can be targeted by several miRNAs. This makes it extremely complex to determine the roles of specific miRNAs in the regulation of translation of mRNA. Recent advancements in microarray technology have made large-scale monitoring of miRNA expression possible. However, the size and complexity of these data sets make them challenging to analyze, and improved algorithms are therefore required to facilitate the analysis. In this paper, we present a novel clustering algorithm that uses an Information Fusion (IF) approach to cluster miRNA data, allowing for multiple types of input data to guide the clustering. For evaluation of the algorithm, we used miRNA expression data from human embryonic stem cells and cardiomyocyte-like cells derived thereof. Clusters obtained when using the multiple input data approach were compared to those generated when using only the expression data. Our results show that it is beneficial to include various types of genomic data as input to the clustering process, since it results in clusters of increased biological relevance.
机译：MicroRNA（miRNA）是小的非编码分子，已显示在调节细胞发育中起关键作用，并参与多种疾病。这些分子通过干扰其靶mRNA来抑制蛋白质的表达，方法是使mRNA分子不稳定或阻止其翻译。已经提出，每个miRNA可以靶向数百个mRNA，并且一个mRNA可以被多个miRNA靶向。这使得确定特定的miRNA在调节mRNA的翻译中的作用极其复杂。微阵列技术的最新进展使得大规模监测miRNA表达成为可能。但是，这些数据集的大小和复杂性使其难以分析，因此需要改进的算法来促进分析。在本文中，我们提出了一种新颖的聚类算法，该算法使用信息融合（IF）方法对miRNA数据进行聚类，从而允许多种类型的输入数据来指导聚类。为了评估该算法，我们使用了来自人类胚胎干细胞及其衍生的心肌样细胞的miRNA表达数据。将使用多输入数据方法时获得的聚类与仅使用表达式数据时所生成的聚类进行比较。我们的结果表明，将各种类型的基因组数据作为聚类过程的输入是有益的，因为它会导致生物相关性增加。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号