...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Network-based prediction for sources of transcriptional dysregulation using latent pathway identification analysis
【24h】

Network-based prediction for sources of transcriptional dysregulation using latent pathway identification analysis

机译：使用潜在途径识别分析的基于网络的转录失调来源预测

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

Understanding the systemic biological pathways and the key cellular mechanisms that dictate disease states, drug response, and altered cellular function poses a significant challenge. Although high-throughput measurement techniques, such as transcriptional profiling, give some insight into the altered state of a cell, they fall far short of providing by themselves a complete picture. Some improvement can be made by using enrichment-based methods to, for example, organize biological data of this sort into collections of dysregulated pathways. However, such methods arguably are still limited to primarily a transcriptional view of the cell. Augmenting these methods still further with networks and additional -omics data has been found to yield pathways that play more fundamental roles. We propose a previously undescribed method for identification of such pathways that takes a more direct approach to the problem than any published to date. Our method, called latent pathway identification analysis (LPIA), looks for statistically significant evidence of dysregulation in a network of pathways constructed in a manner that implicitly links pathways through their common function in the cell. We describe the LPIA methodology and illustrate its effectiveness through analysis of data on (/) metastatic cancer progression, (//) drug treatment in human lung carcinoma cells, and (///) diagnosis of type 2 diabetes. With these analyses, we show that LPIA can successfully identify pathways whose perturbations have latent influences on the tran-scriptionally altered genes.
机译：了解决定疾病状态，药物反应和细胞功能改变的系统生物学途径和关键细胞机制构成了重大挑战。尽管诸如转录图谱之类的高通量测量技术可以洞悉细胞的状态变化，但仍远远不足以提供完整的图像。通过使用基于富集的方法，例如将此类生物学数据组织到失调途径的集合中，可以进行一些改进。然而，可以说，这些方法仍然主要限于细胞的转录视图。通过网络进一步扩展这些方法，并且已发现更多的组学数据可产生起更基本作用的途径。我们提出了一种以前未描述的方法来识别这种途径，该方法比迄今发布的任何方法都更直接地解决了该问题。我们的方法称为潜在途径识别分析（LPIA），它在途径网络中寻找失调的统计显着证据，该途径以通过细胞中其共同功能隐式链接途径的方式构建。我们描述了LPIA方法，并通过分析（/）转移性癌症进展，（//）人肺癌细胞中的药物治疗和（///）2型糖尿病的诊断数据来说明其有效性。通过这些分析，我们表明LPIA可以成功地识别其扰动对转录改变的基因有潜在影响的途径。

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