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CN 41-1437/TS  ISSN 2096-1553

基于伪氨基酸组成和多标记最近邻算法的抗菌肽功能类型预测

王晓 ,  杨鹏鹏 ,  王榕 ,  李辉

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王晓, 杨鹏鹏, 王榕, 等. 基于伪氨基酸组成和多标记最近邻算法的抗菌肽功能类型预测[J]. 轻工学报, 2015, 30(5-6): 85-87. doi: 10.3969/j.issn.2095-476X.2015.5/6.017
引用本文: 王晓, 杨鹏鹏, 王榕, 等. 基于伪氨基酸组成和多标记最近邻算法的抗菌肽功能类型预测[J]. 轻工学报, 2015, 30(5-6): 85-87. doi: 10.3969/j.issn.2095-476X.2015.5/6.017
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WANG Xiao, YANG Peng-peng, WANG Rong and et al. Predicting functional types of antimicrobial peptides with pseudo amino acid composition and multi-label k-nearest neighbor algorithm[J]. Journal of Light Industry, 2015, 30(5-6): 85-87. doi: 10.3969/j.issn.2095-476X.2015.5/6.017
Citation: WANG Xiao, YANG Peng-peng, WANG Rong and et al. Predicting functional types of antimicrobial peptides with pseudo amino acid composition and multi-label k-nearest neighbor algorithm[J]. Journal of Light Industry, 2015, 30(5-6): 85-87. doi: 10.3969/j.issn.2095-476X.2015.5/6.017
shu

基于伪氨基酸组成和多标记最近邻算法的抗菌肽功能类型预测

  • 郑州轻工业学院 计算机与通信工程学院, 河南 郑州 450001

  • 基金项目: 郑州轻工业学院博士科研基金资助项目(2013BSJJ082)
    国家自然科学基金项目(61402422)
    河南省教育厅科学技术研究重点项目(14A520063)

  • 中图分类号: TP273;O811.4

Predicting functional types of antimicrobial peptides with pseudo amino acid composition and multi-label k-nearest neighbor algorithm

  • College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China

  • Received Date: 2015-09-28
    Available Online: 2015-11-15

    CLC number: TP273;O811.4

  • 摘要: 针对多数已有的计算方法无法同时预测抗菌肽的多种功能类型的问题,提出一种基于伪氨基酸组成和多标记最近邻算法的抗菌肽功能类型预测的系统方法:采用伪氨基酸组成抽取抗菌肽序列的特征向量,并且引入多标记最近邻算法作为预测引擎,同时预测抗菌肽的多种功能类型.实验结果表明,本方法显著地提高了预测性能,为该领域的进一步研究提供了一个有用的工具.
    Abstract: In order to solve the problem that most of the existing computational methods can only predict one functional type of antibacterial peptides, a computational prediction method was developed for prediction of multiple functional types of antibacterial peptides based on the pseudo amino acid composition(PseAAC) and multi-label k-nearest neighbor(MLkNN) algorithm.It used the PseAAC to extract feature vector of antimicrobial peptide sequence, introduced the MLkNN algorithm as the prediction engine, and predicted a variety function type of antibacterial peptides simultaneously.Experimental results showed that the proposed method significantly improved the prediction performance, and it provided a useful tool for the further research in this field.
    1. [1]

      Riadh H,Ismail F.Current trends in antimicrobial agent research:chemo-and bioinformatics approaches[J].Drug Discovery Today,2010,15(13/14):540.

    2. [2]

      Fjell C D,Hancock R E,Cherkasov A.AMPer:a database and an automated discovery tool for antimicrobial peptides[J].Bioinformatics,2007,23(9):1148.

    3. [3]

      Lata S,Sharma B K,Raghava G.Analysis and prediction of antibacterial peptides[J].BMC Bioinformatics,2007,8:263.

    4. [4]

      Lata S,Mishra N,Raghava G.AntiBP2:improved version of antibacterial peptide prediction[J].BMC Bioinformatics,2010,11(S1):S19.

    5. [5]

      Wang P,Hu L L,Liu G Y,et al.Prediction of antimicrobial peptides based on sequence alignment and feature selection methods[J].PLoS ONE,2011,6(4):e18476.

    6. [6]

      Khosravian M,Faramarzi F K,Beigi M M,et al.Predicting antibacterial peptides by the concept of Chou's pseudo-amino acid composition and machine learning methods[J].Protein and Peptide Letters,2013,20(2):180.

    7. [7]

      Xiao X,Wang P,Lin W Z,et al.iAMP-2L:a two-level multi-label classifier for identifying antimicrobial peptides and their functional types[J].Analytical Biochemistry,2013,436:168.

    8. [8]

      Chou K C.Prediction of protein cellular attributes using pseudo-amino acid composition[J].Proteins:Structure,Function,and Bioinformatics,2001,43(3):246.

    9. [9]

      Li G Z,Wang X,Hu X,et al.Multilabel learning for protein subcellular location prediction[J].IEEE Transactions on NanoBioscience,2012,11(3):237.

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  • 收稿日期:  2015-09-28
  • 刊出日期:  2015-11-15
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王晓, 杨鹏鹏, 王榕, 等. 基于伪氨基酸组成和多标记最近邻算法的抗菌肽功能类型预测[J]. 轻工学报, 2015, 30(5-6): 85-87. doi: 10.3969/j.issn.2095-476X.2015.5/6.017
引用本文: 王晓, 杨鹏鹏, 王榕, 等. 基于伪氨基酸组成和多标记最近邻算法的抗菌肽功能类型预测[J]. 轻工学报, 2015, 30(5-6): 85-87. doi: 10.3969/j.issn.2095-476X.2015.5/6.017
shu
WANG Xiao, YANG Peng-peng, WANG Rong and et al. Predicting functional types of antimicrobial peptides with pseudo amino acid composition and multi-label k-nearest neighbor algorithm[J]. Journal of Light Industry, 2015, 30(5-6): 85-87. doi: 10.3969/j.issn.2095-476X.2015.5/6.017
Citation: WANG Xiao, YANG Peng-peng, WANG Rong and et al. Predicting functional types of antimicrobial peptides with pseudo amino acid composition and multi-label k-nearest neighbor algorithm[J]. Journal of Light Industry, 2015, 30(5-6): 85-87. doi: 10.3969/j.issn.2095-476X.2015.5/6.017
shu

基于伪氨基酸组成和多标记最近邻算法的抗菌肽功能类型预测

  • 郑州轻工业学院 计算机与通信工程学院, 河南 郑州 450001
  • 收稿日期: 2015-09-28
  • 网络出版日期: 2015-11-15
基金项目:  郑州轻工业学院博士科研基金资助项目(2013BSJJ082)国家自然科学基金项目(61402422)河南省教育厅科学技术研究重点项目(14A520063)

摘要: 针对多数已有的计算方法无法同时预测抗菌肽的多种功能类型的问题,提出一种基于伪氨基酸组成和多标记最近邻算法的抗菌肽功能类型预测的系统方法:采用伪氨基酸组成抽取抗菌肽序列的特征向量,并且引入多标记最近邻算法作为预测引擎,同时预测抗菌肽的多种功能类型.实验结果表明,本方法显著地提高了预测性能,为该领域的进一步研究提供了一个有用的工具.

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