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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/36631

Title: Analysis and Prediction of Highly Effective Antiviral Peptides Based on Random Forests
Authors: Chang;Kuan-Yuan
張光遠
Contributors: NTOU:Department of Computer Science and Engineering
國立臺灣海洋大學:資訊工程學系
Keywords: Peptides;Amino acid analysis;Hepatitis C virus;Lysine;Physicochemical properties;Rabies virus;West Nile virus;Viral structure
Date: 2013-08
Issue Date: 2015-05-19T06:34:58Z
Publisher: PLOS ONE
Abstract: The goal of this study was to examine and predict antiviral peptides. Although antiviral peptides hold great potential in antiviral drug discovery, little is done in antiviral peptide prediction. In this study, we demonstrate that a physicochemical model using random forests outperform in distinguishing antiviral peptides. On the experimental benchmark, our physicochemical model aided with aggregation and secondary structural features reaches 90% accuracy and 0.79 Matthew's correlation coefficient, which exceeds the previous models. The results suggest that aggregation could be an important feature for identifying antiviral peptides. In addition, our analysis reveals the characteristics of the antiviral peptides such as the importance of lysine and the abundance of α-helical secondary structures.
Relation: 8(8), e7016
URI: http://ntour.ntou.edu.tw/handle/987654321/36631
Appears in Collections:[資訊工程學系] 期刊論文

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