Soft Computing

, Volume 22, Issue 10, pp 3373–3381 | Cite as

An improved efficient rotation forest algorithm to predict the interactions among proteins

  • Lei Wang
  • Zhu-Hong You
  • Shi-Xiong Xia
  • Xing Chen
  • Xin Yan
  • Yong Zhou
  • Feng Liu
Methodologies and Application

Abstract

Protein–protein interactions (PPIs) are the basis to interpret biological mechanisms of life activity, and play vital roles in the execution of various cellular processes. The development of computer technology provides a new way for the effective prediction of PPIs and greatly arouses people’s interest. The challenge of this task is that PPIs data is typically represented in high-dimensional and is likely to contain noise, which will greatly affect the performance of the classifier. In this paper, we propose a novel feature weighted rotation forest algorithm (FWRF) to solve this problem. We calculate the weight of the feature by the \(\chi ^{2}\) statistical method and remove the low weight value features according to the selection rate. With this FWRF algorithm, the proposed method can eliminate the interference of useless information and make full use of the useful features to predict the interactions among proteins. In cross-validation experiment, our method obtained excellent prediction performance with the average accuracy, precision, sensitivity, MCC and AUC of 91.91, 92.51, 91.22, 83.84 and 91.60% on the H. pylori data set. We compared our method with other existing methods and the well-known classifiers, such as SVM and original rotation forest on the H. pylori data set. In addition, in order to demonstrate the ability of the FWRF algorithm, we also verified on the Yeast data set. The experimental results show that our method is more effective and robust in predicting PPIs. As a web server, the source code, H. pylori data sets and Yeast data sets used in this article are freely available at http://202.119.201.126:8888/FWRF/.

Keywords

Rotation forest Ensemble learning Bioinformatics Support vector machine 

Notes

Acknowledgements

This work is supported by the Fundamental Research Funds for the Central Universities (2017XKQY083).

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Lei Wang
    • 1
    • 5
  • Zhu-Hong You
    • 2
  • Shi-Xiong Xia
    • 1
  • Xing Chen
    • 3
  • Xin Yan
    • 4
  • Yong Zhou
    • 1
  • Feng Liu
    • 6
  1. 1.School of Computer Science and TechnologyChina University of Mining and TechnologyXuzhouChina
  2. 2.Xinjiang Technical Institute of Physics and ChemistryChinese Academy of ScienceUrumqiChina
  3. 3.School of Information and Control EngineeringChina University of Mining and TechnologyXuzhouChina
  4. 4.School of Foreign LanguagesZaozhuang UniversityZaozhuangChina
  5. 5.College of Information Science and EngineeringZaozhuang UniversityZaozhuangChina
  6. 6.China National Coal AssociationBeijingChina

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