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A highly accurate protein structural class prediction approach using auto cross covariance transformation and recursive feature elimination

Paper ID Volume ID Publish Year Pages File Format Full-Text
14976 1365 2015 6 PDF Available
Title
A highly accurate protein structural class prediction approach using auto cross covariance transformation and recursive feature elimination
Abstract

•Prediction performance of protein structural class has been improved.•A high-quality feature extraction technique has been designed.•A recursive feature selection has been used to reduce feature abundance.

Structural class characterizes the overall folding type of a protein or its domain. Many methods have been proposed to improve the prediction accuracy of protein structural class in recent years, but it is still a challenge for the low-similarity sequences. In this study, we introduce a feature extraction technique based on auto cross covariance (ACC) transformation of position-specific score matrix (PSSM) to represent a protein sequence. Then support vector machine-recursive feature elimination (SVM-RFE) is adopted to select top K features according to their importance and these features are input to a support vector machine (SVM) to conduct the prediction. Performance evaluation of the proposed method is performed using the jackknife test on three low-similarity datasets, i.e., D640, 1189 and 25PDB. By means of this method, the overall accuracies of 97.2%, 96.2%, and 93.3% are achieved on these three datasets, which are higher than those of most existing methods. This suggests that the proposed method could serve as a very cost-effective tool for predicting protein structural class especially for low-similarity datasets.

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Keywords
Low-similarity; Position-specific score matrix; Auto cross covariance; Support vector machine; Recursive feature elimination
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A highly accurate protein structural class prediction approach using auto cross covariance transformation and recursive feature elimination
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 59, Part A, December 2015, Pages 95–100
Authors
, , , , ,
Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us