...
首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Investigating noise tolerance in an efficient engine for inferring biological regulatory networks
【24h】

Investigating noise tolerance in an efficient engine for inferring biological regulatory networks

机译:研究用于推断生物调控网络的高效发动机的噪声耐受性

获取原文
获取原文并翻译 | 示例
           

摘要

Biological systems are composed of biomolecules such as genes, proteins, metabolites, and signaling components, which interact in complex networks. To understand complex biological systems, it is important to be capable of inferring regulatory networks from experimental time series data. In previous studies, we developed efficient numerical optimization methods for inferring these networks, but we have yet to test the performance of our methods when considering the error (noise) that is inherent in experimental data. In this study, we investigated the noise tolerance of our proposed inferring engine. We prepared the noise data using the Langevin equation, and compared the performance of our method with that of alternative optimization methods.
机译:生物系统由生物分子组成,例如基因,蛋白质,代谢产物和信号传导成分,它们在复杂的网络中相互作用。要了解复杂的生物系统,重要的是能够从实验时间序列数据中推断出调控网络。在先前的研究中,我们开发了用于推断这些网络的有效数值优化方法,但在考虑实验数据固有的误差(噪声)时,我们尚未测试方法的性能。在这项研究中,我们调查了我们提出的推理引擎的噪声容忍度。我们使用Langevin方程准备了噪声数据,并将我们的方法的性能与替代性优化方法的性能进行了比较。

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