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Prediction of protein structural classes based on feature selection technique

  • Hui Ding
  • Hao Lin
  • Wei Chen
  • Zi-Qiang Li
  • Feng-Biao Guo
  • Jian Huang
  • Nini Rao
Article

Abstract

The prediction of protein structural classes is beneficial to understanding folding patterns, functions and interactions of proteins. In this study, we proposed a feature selection-based method to accurately predict protein structural classes. Three datasets with sequence identity lower than 25% were used to test the prediction performance of the method. Through jackknife cross-validation, we have verified that the overall accuracies of these three datasets are 92.1%, 89.7% and 84.0%, respectively. The proposed method is more efficient and accurate than other existing methods. The present study will offer an excellent alternative to other methods for predicting protein structural classes.

Key words

protein structural class feature selection technique support vector machine tetrapeptide 

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Copyright information

© International Association of Scientists in the Interdisciplinary Areas and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.Department of Physics, Center for Genomics and Computational Biology, College of SciencesHebei United UniversityTangshanChina
  3. 3.School of information and EngineeringSichuan Agricultural UniversityYaanChina

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