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Identification of Hotspots in Protein-Protein Interactions Based on Recursive Feature Elimination

  • Xiaoli Lin
  • Xiaolong Zhang
  • Fengli Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10954)

Abstract

The study of protein-protein interactions and protein structure through computational methods is critical to understand protein function. Hot spot residues play an important role in bioinformatics to reveal life activities. However, conventional hot spots prediction methods may face great challenges. This paper proposes a hot spot prediction method based on feature selection method SVM-RFE to improve the training performance. SMOTE based oversampling is used to adds new samples to avoid an overfitting classifier. SVM-RFE is then invoked to obtained optimal feature subset. Finally, a feature-based SVM is created to predict the hot spots. Experimental results indicate that the performance of hot spots prediction has been significantly improved compared with the previous methods.

Keywords

Protein-Protein interactions Hot spots SVM-RFE Classification 

Notes

Acknowledgment

The authors thank the members of Machine Learning and Artificial Intelligence Laboratory, School of Computer Science and Technology, Wuhan University of Science and Technology, for their helpful discussion within seminars. This work was supported in part by National Natural Science Foundation of China (No. 61502356, 61273225), by Hubei Province Natural Science Foundation of China (No. 2018CFB526).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, School of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina
  2. 2.Information and Engineering Department of City CollegeWuhan University of Science and TechnologyWuhanChina

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