Next Article in Journal
Physiological Dynamics in Demyelinating Diseases: Unraveling Complex Relationships through Computer Modeling
Previous Article in Journal
Effect of Hepatitis C Virus Genotype 1b Core and NS5A Mutations on Response to Peginterferon Plus Ribavirin Combination Therapy
Open AccessArticle

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

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Back to TopTop