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• W2000371303 abstract "Apoptosis proteins have a central role in the development and homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. Based on the idea of coarse-grained description and grouping in physics, a new feature extraction method with grouped weight for protein sequence is presented, and applied to apoptosis protein subcellular localization prediction associated with support vector machine. For the same training dataset and the same predictive algorithm, the overall prediction accuracy of our method in Jackknife test is 13.2% and 15.3% higher than the accuracy based on the amino acid composition and instability index. Especially for the else class apoptosis proteins, the increment of prediction accuracy is 41.7 and 33.3 percentile, respectively. The experiment results show that the new feature extraction method is efficient to extract the structure information implicated in protein sequence and the method has reached a satisfied performance despite its simplicity. The overall prediction accuracy of EBGW_SVM model on dataset ZD98 reach 92.9% in Jackknife test, which is 8.2–20.4 percentile higher than other existing models. For a new dataset ZW225, the overall prediction accuracy of EBGW_SVM achieves 83.1%. Those implied that EBGW_SVM model is a simple but efficient prediction model for apoptosis protein subcellular location prediction." @default.
• W2000371303 created "2016-06-24" @default.
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• W2000371303 creator A5058980858 @default.
• W2000371303 creator A5075704897 @default.
• W2000371303 date "2006-10-17" @default.
• W2000371303 modified "2023-09-23" @default.
• W2000371303 title "A novel method for apoptosis protein subcellular localization prediction combining encoding based on grouped weight and support vector machine" @default.
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• W2000371303 doi "https://doi.org/10.1016/j.febslet.2006.10.017" @default.
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