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Int. J. Mol. Sci. 2016, 17(1), 15; doi:10.3390/ijms17010015

Prediction of Protein Structural Class Based on Gapped-Dipeptides and a Recursive Feature Selection Approach

1
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
2
College of Food Science & Technology, Shanghai Ocean University, Shanghai 201306, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Christo Z. Christov
Received: 20 November 2015 / Revised: 15 December 2015 / Accepted: 18 December 2015 / Published: 24 December 2015
(This article belongs to the Section Physical Chemistry, Theoretical and Computational Chemistry)
View Full-Text   |   Download PDF [328 KB, uploaded 24 December 2015]   |  

Abstract

The prior knowledge of protein structural class may offer useful clues on understanding its functionality as well as its tertiary structure. Though various significant efforts have been made to find a fast and effective computational approach to address this problem, it is still a challenging topic in the field of bioinformatics. The position-specific score matrix (PSSM) profile has been shown to provide a useful source of information for improving the prediction performance of protein structural class. However, this information has not been adequately explored. To this end, in this study, we present a feature extraction technique which is based on gapped-dipeptides composition computed directly from PSSM. Then, a careful feature selection technique is performed based on support vector machine-recursive feature elimination (SVM-RFE). These optimal features are selected to construct a final predictor. The results of jackknife tests on four working datasets show that our method obtains satisfactory prediction accuracies by extracting features solely based on PSSM and could serve as a very promising tool to predict protein structural class. View Full-Text
Keywords: feature selection; gapped-dipeptide; position-specific score matrix; protein structural class; recursive feature elimination; support vector machine feature selection; gapped-dipeptide; position-specific score matrix; protein structural class; recursive feature elimination; support vector machine
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Liu, T.; Qin, Y.; Wang, Y.; Wang, C. Prediction of Protein Structural Class Based on Gapped-Dipeptides and a Recursive Feature Selection Approach. Int. J. Mol. Sci. 2016, 17, 15.

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