JOURNAL OF LIGHT INDUSTRY

CN 41-1437/TS  ISSN 2096-1553

Volume 31 Issue 4
July 2016
Article Contents
WANG Xiao, LI Hui and ZHAI Yun-qing. Predicting subcellular localization of apoptosis protein based on ensemble learning and Gene Ontology annotation database[J]. Journal of Light Industry, 2016, 31(4): 95-101. doi: 10.3969/j.issn.2096-1553.2016.4.014
Citation: WANG Xiao, LI Hui and ZHAI Yun-qing. Predicting subcellular localization of apoptosis protein based on ensemble learning and Gene Ontology annotation database[J]. Journal of Light Industry, 2016, 31(4): 95-101. doi: 10.3969/j.issn.2096-1553.2016.4.014 shu

Predicting subcellular localization of apoptosis protein based on ensemble learning and Gene Ontology annotation database

  • Received Date: 2016-03-20
    Available Online: 2016-07-15
  • In order to deal with the problem that the prediction accuracy of subcellular localization of apoptosis proteins is not high, a method of predicting subcellular localization of apoptosis protein based on ensemble learning and Gene Ontology (GO) annotation database was proposed. This method utilized the GO features of apoptosis proteins and their homologous proteins combined with the two layer integration strategy to predict subcellular localization of apoptosis proteins. In the first layer, several sets of feature vectors were formulated by the different number of homologous proteins, then it selected the distance weighted K-nearest neighbor classifier as individual classifier, trained sub-prediction models, and integrated these models by majority voting. In the second layer, the prediction model of the first layer was used as the sub-prediction model, and it integrated the different nearest neighbors' sub-prediction models by the majority voting. The results of Jackknife test showed that prediction accuracy of the method reaches 96.2% on the CL317 apoptosis proteins dataset, which was superior to other methods. In addition,this method could reduce the impact of the data imbalance.
  • 加载中
    1. [1]

      EVAN G,LITTLEWOOD T.A matter of life and cell death[J].Science,1998,281(5381):1317.

    2. [2]

      REED J C,PATERNOSTRO G.Postmitochondrial regulation of apoptosis during heart failure[J].Proc Natl Acad Sci USA,1999,96(14):7614.

    3. [3]

      JACOBSON M D,WEIL M,RAFF M C.Programmed cell death in animal development[J].Cell,1997,88(3):347.

    4. [4]

      SCHULZ J B,WELLER M,MOSKOWITZ M A.Caspases as treatment targets in stroke and neurodegenerative diseases[J].Annals of Neurology,1999,45(4):421.

    5. [5]

      SUZUKI M,YOULE R J.Structure of Bax:Coregulation of dimer formation and intracellular localization[J].Cell,2000,103(4):645.

    6. [6]

      张松,黄波,夏学峰,等.蛋白质亚细胞定位的生物信息学研究[J].生物化学与生物物理进展,2007(6):573.

    7. [7]

      ZHOU G P,Doctor K.Subcellular location prediction of apoptosis proteins[J].Proteins:Structure,Function and Genetics,2003,50(1):44.

    8. [8]

      BULASHEVSKA A,EILS R.Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains[J].BMC Bioinformatics,2006,7(1):298.

    9. [9]

      ZHANG Z H,WANG Z H,ZHANG Z R,et al.A novel method for apoptosis protein subcellular localization prediction combining encoding based on grouped weight and support vector machine[J].FEBS Letters,2006,580(26):6169.

    10. [10]

      CHEN Y L,LI Q Z.Prediction of the subcellular location of apoptosis proteins[J].Journal of Theoretical Biology,2007,245(4):775.

    11. [11]

      CHEN Y L,ZHONG Q Z.Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition[J].Journal of Theoretical Biology,2007,248(2):377.

    12. [12]

      DING Y,ZHANG T.Using Chou's pseudo amino acid composition to predict subcellular localization of apoptosis proteins:an approach with immune genetic algorithm-based ensemble classifier[J].Pattern Recognition Letters,2008,29(13):1887.

    13. [13]

      ZHANG L,LIAO B,LI D,et al.A novel representation for apoptosis protein subcellular localization prediction using support vector machine[J].Journal of Theoretical Biology,2009,259(2):361.

    14. [14]

      QIU J,LUO S,HUANG J,et al.Predicting subcellular location of apoptosis proteins based on wavelet transform and support vector machine[J].Amino Acids,2010,38(4):1201.

    15. [15]

      LIU T G,ZHENG X Q,WANG J,et al.Prediction of subcellular location of apoptosis proteins using pseudo amino acid composition:an approach from auto covariance transformation[J].Protein&Peptide Letters,2010,17(10):1263.

    16. [16]

      LIN H,WANG H,DING H,et al.Prediction of subcellular localization of apoptosis protein using Chou's pseudo amino acid composition[J].Acta Biotheoretica,2009,57(3):321.

    17. [17]

      GU Q,DING Y,JIANG X,et al.Prediction of subcellular location apoptosis proteins with ensemble classifier and feature selection[J].Amino Acids,2010,38(4):975.

    18. [18]

      YU X,ZHENG X,LIU T,et al.Predicting subcellular location of apoptosis proteins with pseudo amino acid composition:approach from amino acid substitution matrix and auto covariance transformation[J].Amino Acids,2012,42(5):1619.

    19. [19]

      SARAVANAN V,LAKSHMI P T V.APSLAP:an adaptive boosting technique for predicting subcellular localization of apoptosis protein[J].Acta Biotheoretica,2013,61(4):481.

    20. [20]

      LIU T,TAO P,LI X,et al.Prediction of subcellular location of apoptosis proteins combining tri-gram encoding based on PSSM and recursive feature elimination[J].Journal of Theoretical Biology,2015,366:8.

    21. [21]

      HARRIS M A,CLARK J,IRELAND A,et al.The Gene Ontology (GO) database and informatics resource[J].Nucleic Acids Research,2004,32(Database issue):D258.

    22. [22]

      CAMON E,MAGRANE M,BARRELL D,et al.The Gene Ontology Annotation (GOA) Database:sharing knowledge in Uniprot with Gene Ontology[J].Nucleic Acids Research,2004,32(Database issue):D262.

    23. [23]

      CAMON E,MAGRANE M,BARRELL D,et al.The Gene Ontology Annotation (GOA) Project:Implementation of GO in SWISS-PROT,TrEMBL,and InterPro[J].Genome Research,2003,13(4):662.

Article Metrics

Article views(1250) PDF downloads(45) Cited by()

Ralated
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return