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Application of Decision Tree for the Classification of Antimicrobial Peptide  

Lee, Su Yeon (Interdisciplinary Program in Bioinformatics, Laboratory of Molecular Genetics, School of Biological Sciences, Institute of Molecular Biology and Genetics, and Seoul National University)
Kim, Sunkyu (Laboratory of Molecular Genetics, School of Biological Sciences, Institute of Molecular Biology and Genetics, and Seoul National University)
Kim, Sukwon S. (Laboratory of Molecular Genetics, School of Biological Sciences, Institute of Molecular Biology and Genetics, and Seoul National University)
Cha, Seon Jeong (Interdisciplinary Program in Bioinformatics, Laboratory of Molecular Genetics, School of Biological Sciences, Institute of Molecular Biology and Genetics, and Seoul National University)
Kwon, Young Keun (Optimization Laboratory, School of Computer Science and Engineering, Seoul National University)
Moon, Byung-Ro (Interdisciplinary Program in Bioinformatics, Optimization Laboratory, School of Computer Science and Engineering, Seoul National University)
Lee, Byeong Jae (Interdisciplinary Program in Bioinformatics, Laboratory of Molecular Genetics, School of Biological Sciences, Institute of Molecular Biology and Genetics, and Seoul National University)
Publication Information
Genomics & Informatics / v.2, no.3, 2004 , pp. 121-125 More about this Journal
Abstract
The purpose of this study was to investigate the use of decision tree for the classification of antimicrobial peptides. The classification was based on the activities of known antimicrobial peptides against common microbes including Escherichia coli and Staphylococcus aureus. A feature selection was employed to select an effective subset of features from available attribute sets. Sequential applications of decision tree with 17 nodes with 9 leaves and 13 nodes with 7 leaves provided the classification rates of $76.74\%$ and $74.66\%$ against E. coli and S. aureus, respectively. Angle subtended by positively charged face and the positive charge commonly gave higher accuracies in both E. coli and S. aureusdatasets. In this study, we describe a successful application of decision tree that provides the understanding of the effects of physicochemical characteristics of peptides on bacterial membrane.
Keywords
decision tree; classification; antimicrobial peptides;
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