...
  • 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 >A Framework for Incorporating Functional Interrelationships into Protein Function Prediction Algorithms
【24h】

A Framework for Incorporating Functional Interrelationships into Protein Function Prediction Algorithms

机译：将功能相互关系纳入蛋白质功能预测算法的框架

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

The functional annotation of proteins is one of the most important tasks in the post-genomic era. Although many computational approaches have been developed in recent years to predict protein function, most of these traditional algorithms do not take interrelationships among functional terms into account, such as different GO terms usually coannotate with some common proteins. In this study, we propose a new functional similarity measure in the form of Jaccard coefficient to quantify these interrelationships and also develop a framework for incorporating GO term similarity into protein function prediction process. The experimental results of cross-validation on S. cerevisiae and Homo sapiens data sets demonstrate that our method is able to improve the performance of protein function prediction. In addition, we find that small size terms associated with a few of proteins obtain more benefit than the large size ones when considering functional interrelationships. We also compare our similarity measure with other two widely used measures, and results indicate that when incorporated into function prediction algorithms, our proposed measure is more effective. Experiment results also illustrate that our algorithms outperform two previous competing algorithms, which also take functional interrelationships into account, in prediction accuracy. Finally, we show that our method is robust to annotations in the database which are not complete at present. These results give new insights about the importance of functional interrelationships in protein function prediction.
机译：蛋白质的功能注释是后基因组时代最重要的任务之一。尽管近年来已经开发出许多计算方法来预测蛋白质功能，但是这些传统算法大多数都没有考虑功能项之间的相互关系，例如不同的GO项通常与某些常见的蛋白质并存。在这项研究中，我们提出了一种新的功能相似性度量，以Jaccard系数的形式量化这些相互关系，并且还开发了将GO术语相似性纳入蛋白质功能预测过程的框架。对酿酒酵母和智人数据集进行交叉验证的实验结果表明，我们的方法能够提高蛋白质功能预测的性能。此外，我们发现与少量蛋白质相关的小尺寸术语在考虑功能相互关系时比大尺寸术语获得更多的好处。我们还将相似性度量与其他两种广泛使用的度量进行了比较，结果表明，将其纳入函数预测算法后，我们提出的度量更加有效。实验结果还表明，在预测准确性方面，我们的算法优于以前的两个竞争算法，后者也考虑了功能相互关系。最后，我们证明了我们的方法对于当前尚不完善的数据库中的注释具有鲁棒性。这些结果为功能相互关系在蛋白质功能预测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