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Int. J. Mol. Sci. 2015, 16(9), 21191-21214; doi:10.3390/ijms160921191

An Effective Antifreeze Protein Predictor with Ensemble Classifiers and Comprehensive Sequence Descriptors

1
School of Control Science and Engineering, Shandong University, Jinan 250061, China
2
School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
*
Author to whom correspondence should be addressed.
Academic Editor: Christo Z. Christov
Received: 3 August 2015 / Revised: 18 August 2015 / Accepted: 26 August 2015 / Published: 7 September 2015
(This article belongs to the Section Physical Chemistry, Theoretical and Computational Chemistry)
View Full-Text   |   Download PDF [423 KB, uploaded 14 September 2015]   |  

Abstract

Antifreeze proteins (AFPs) play a pivotal role in the antifreeze effect of overwintering organisms. They have a wide range of applications in numerous fields, such as improving the production of crops and the quality of frozen foods. Accurate identification of AFPs may provide important clues to decipher the underlying mechanisms of AFPs in ice-binding and to facilitate the selection of the most appropriate AFPs for several applications. Based on an ensemble learning technique, this study proposes an AFP identification system called AFP-Ensemble. In this system, random forest classifiers are trained by different training subsets and then aggregated into a consensus classifier by majority voting. The resulting predictor yields a sensitivity of 0.892, a specificity of 0.940, an accuracy of 0.938 and a balanced accuracy of 0.916 on an independent dataset, which are far better than the results obtained by previous methods. These results reveal that AFP-Ensemble is an effective and promising predictor for large-scale determination of AFPs. The detailed feature analysis in this study may give useful insights into the molecular mechanisms of AFP-ice interactions and provide guidance for the related experimental validation. A web server has been designed to implement the proposed method. View Full-Text
Keywords: antifreeze proteins; ensemble method; random forest; majority voting antifreeze proteins; ensemble method; random forest; majority voting
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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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Yang, R.; Zhang, C.; Gao, R.; Zhang, L. An Effective Antifreeze Protein Predictor with Ensemble Classifiers and Comprehensive Sequence Descriptors. Int. J. Mol. Sci. 2015, 16, 21191-21214.

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