...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Molecular Sciences >Detection of Interactions between Proteins through Rotation Forest and Local Phase Quantization Descriptors
【24h】

Detection of Interactions between Proteins through Rotation Forest and Local Phase Quantization Descriptors

机译：通过旋转森林和局部相位量化描述符检测蛋白质之间的相互作用

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Protein-Protein Interactions (PPIs) play a vital role in most cellular processes. Although many efforts have been devoted to detecting protein interactions by high-throughput experiments, these methods are obviously expensive and tedious. Targeting these inevitable disadvantages, this study develops a novel computational method to predict PPIs using information on protein sequences, which is highly efficient and accurate. The improvement mainly comes from the use of the Rotation Forest (RF) classifier and the Local Phase Quantization (LPQ) descriptor from the Physicochemical Property Response (PR) Matrix of protein amino acids. When performed on three PPI datasets including Saccharomyces cerevisiae , Homo sapiens , and Helicobacter pylori , we obtained good results of average accuracies of 93.8%, 97.96%, and 89.47%, which are much better than in previous studies. Extensive validations have also been explored to evaluate the performance of the Rotation Forest ensemble classifier with the state-of-the-art Support Vector Machine classifier. These promising results indicate that the proposed method might play a complementary role for future proteomics research.
机译：蛋白质-蛋白质相互作用（PPI）在大多数细胞过程中起着至关重要的作用。尽管人们已经为通过高通量实验检测蛋白质相互作用做出了很多努力，但是这些方法显然昂贵且乏味。针对这些不可避免的弊端，本研究开发了一种新颖的计算方法，可以利用蛋白质序列信息来预测PPI，该方法高效且准确。改进主要来自使用旋转森林（RF）分类器和蛋白质氨基酸的理化特性响应（PR）矩阵中的局部相位量化（LPQ）描述符。当对啤酒酵母，智人和幽门螺杆菌这三个PPI数据集执行时，我们获得了93.8％，97.96％和89.47％的平均准确度的良好结果，这比以前的研究要好得多。还已经探索了广泛的验证方法，以评估采用最新技术的支持向量机分类器的“旋转森林”集成分类器的性能。这些有希望的结果表明，所提出的方法可能对未来的蛋白质组学研究起到补充作用。

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