iAFP-Ense: An Ensemble Classifier for Identifying Antifreeze Protein by Incorporating Grey Model and PSSM into PseAAC

iAFP-Ense: An Ensemble Classifier for Identifying Antifreeze Protein by Incorporating Grey Model... Antifreeze proteins (AFPs), known as thermal hysteresis proteins, are ice-binding proteins. AFPs have been found in many fields such as in vertebrates, invertebrates, plants, bacteria, and fungi. Although the function of AFPs is common, the sequences and structures of them show a high degree of diversity. AFPs can be adsorbed in ice crystal surface and inhibit the growth of ice crystals in solution. However, the interaction between AFPs and ice crystal is not completely known for human beings. It is vitally significant to propose an automated means as a high-throughput tool to timely identify the AFPs. Analyzing physicochemical characteristics of AFPs sequences is very significant to understand the ice-protein interaction. In this manuscript, a predictor called “iAFP-Ense” was developed. The operation engine to run the AFPs prediction is an ensemble classifier formed by a voting system to fuse eleven different random forest classifiers based on feature extraction. We also compare our predictor with the AFP-PseAAC via the tenfold cross-validation on the same benchmark dataset. The comparison with the existing methods indicates the new predictor is very promising, meaning that many important key features which are deeply hidden in complicated protein sequences. The predictor used in this article is freely available at http://www.jci-bioinfo.cn/iAFP-Ense . http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Membrane Biology Springer Journals

iAFP-Ense: An Ensemble Classifier for Identifying Antifreeze Protein by Incorporating Grey Model and PSSM into PseAAC

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Springer US
Copyright © 2016 by Springer Science+Business Media New York
Life Sciences; Biochemistry, general; Human Physiology
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