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An Ensemble Classifier of Support Vector Machines Used to Predict Protein Structural Classes by Fusing Auto Covariance and Pseudo-Amino Acid Composition

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Abstract

The purpose of this article is to identify protein structural classes by using support vector machine (SVM) ensemble classifier, which is very efficient in enhancing prediction performance. Firstly, auto covariance (AC) and pseudo-amino acid composition (PseAAC) were used in protein representation. AC focuses on adjacent effects and PseAA composition takes sequence order patterns into account. Secondly, SVMs were trained on the datasets represented by different descriptors. The last, ensemble classifier, which constructed on the individual classifiers through a voting strategy, gave the final prediction results. Meanwhile, very promising prediction accuracy 93.14% was obtained by Jackknife test. The experimental results showed that the ensemble system can improve the prediction performance greatly and generate more stable and safer predictors. The current method featured by fusing the protein primary sequence information transferred by AC and described by protein PseAA composition may play an important complementary role in other related applications.

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Abbreviations

AC:

Auto covariance

SVM:

Support vector machine

PseAA:

Pseudo-amino acid

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Acknowledgments

This article was supported by the National Natural Science Foundation of China (No. 20775052).

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Authors and Affiliations

  1. College of Chemistry, Sichuan University, 610064, Chengdu, China

    Jiang Wu, Meng-Long Li, Le-Zheng Yu & Chao Wang

  2. Department of Information Technology, Yulin University, 719000, Yulin City, China

    Jiang Wu

Authors
  1. Jiang Wu
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  2. Meng-Long Li
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  3. Le-Zheng Yu
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  4. Chao Wang
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Correspondence to Meng-Long Li.

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Wu, J., Li, ML., Yu, LZ. et al. An Ensemble Classifier of Support Vector Machines Used to Predict Protein Structural Classes by Fusing Auto Covariance and Pseudo-Amino Acid Composition. Protein J 29, 62–67 (2010). https://doi.org/10.1007/s10930-009-9222-z

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