...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Protein Complexes Discovery Based on Protein-Protein Interaction Data via a Regularized Sparse Generative Network Model
【24h】

Protein Complexes Discovery Based on Protein-Protein Interaction Data via a Regularized Sparse Generative Network Model

机译：通过正则稀疏生成网络模型基于蛋白质-蛋白质相互作用数据的蛋白质复合物发现

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

Detecting protein complexes from protein interaction networks is one major task in the postgenome era. Previous developed computational algorithms identifying complexes mainly focus on graph partition or dense region finding. Most of these traditional algorithms cannot discover overlapping complexes which really exist in the protein-protein interaction (PPI) networks. Even if some density-based methods have been developed to identify overlapping complexes, they are not able to discover complexes that include peripheral proteins. In this study, motivated by recent successful application of generative network model to describe the generation process of PPI networks and to detect communities from social networks, we develop a regularized sparse generative network model (RSGNM), by adding another process that generates propensities using exponential distribution and incorporating Laplacian regularizer into an existing generative network model, for protein complexes identification. By assuming that the propensities are generated using exponential distribution, the estimators of propensities will be sparse, which not only has good biological interpretation but also helps to control the overlapping rate among detected complexes. And the Laplacian regularizer will lead to the estimators of propensities more smooth on interaction networks. Experimental results on three yeast PPI networks show that RSGNM outperforms six previous competing algorithms in terms of the quality of detected complexes. In addition, RSGNM is able to detect overlapping complexes and complexes including peripheral proteins simultaneously. These results give new insights about the importance of generative network models in protein complexes identification.
机译：从蛋白质相互作用网络检测蛋白质复合物是后基因组时代的一项主要任务。先前开发的用于识别复合物的计算算法主要集中在图分区或密集区域查找上。这些传统算法大多数都无法发现蛋白质-蛋白质相互作用（PPI）网络中确实存在的重叠复合物。即使已开发出一些基于密度的方法来识别重叠的复合物，它们也无法发现包含外围蛋白的复合物。在这项研究中，受最近成功使用生成网络模型来描述PPI网络的生成过程并从社交网络中检测社区的动机启发，我们通过添加另一个使用指数生成倾向的过程来开发正则化的稀疏生成网络模型（RSGNM）。分布并将Laplacian正则化函数合并到现有的生成网络模型中，用于蛋白质复合物的鉴定。通过假设倾向是使用指数分布生成的，倾向的估计量将是稀疏的，这不仅具有良好的生物学解释，而且还有助于控制检测到的复合物之间的重叠率。拉普拉斯正则化函数将导致交互网络上的倾向性估算器更加平滑。在三个酵母PPI网络上的实验结果表明，就检测到的复合物的质量而言，RSGNM优于六个竞争算法。此外，RSGNM能够同时检测重叠的复合物和包括外围蛋白的复合物。这些结果为生成网络模型在蛋白质复合物鉴定中的重要性提供了新的见解。

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