Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach.

The prediction of apoptosis protein subcellular localization plays an important role in understanding the progress in cell proliferation and death. Recently computational approaches to this issue have become very popular, since the traditional biological experiments are so costly and time-consuming...

Descripción completa

Detalles Bibliográficos
Publicado en:BioMed Research International pp. 1 - 10
Autores principales: Chen, Xingjian, Hu, Xuejiao, Yi, Wenxin, Zou, Xiang, Xue, Wei
Formato: equations & formulas research tables/charts Journal Article
Publicado: Wiley-Blackwell 1/30/2019
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=134372686&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 134372686
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        23146133
        FT2T
      jtl: BioMed Research International
      issn: 23146133
      maglogo: N
    pubinfo:
      dt: 1/30/2019
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
    artinfo:
      ui:
        134372686
        134372686
        134372686
        10.1155/2019/2436924
        134372686
      ppf: 1
      ppct: 9
      formats:
        fmt:
          – @attributes:
              type: T
          – @attributes:
              type: P
      tig:
        atl: Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach.
      aug:
        au:
          Chen, Xingjian
          Hu, Xuejiao
          Yi, Wenxin
          Zou, Xiang
          Xue, Wei
        affil: College of Information Science and Technology, Nanjing Agricultural University, Nanjing, 210095, China
      sug:
        subj:
          Apoptosis
          Sequence Analysis Methods
          Algorithms
          Human
          Machine Learning
          Validity
      ab: The prediction of apoptosis protein subcellular localization plays an important role in understanding the progress in cell proliferation and death. Recently computational approaches to this issue have become very popular, since the traditional biological experiments are so costly and time-consuming that they cannot catch up with the growth rate of sequence data anymore. In order to improve the prediction accuracy of apoptosis protein subcellular localization, we proposed a sparse coding method combined with traditional feature extraction algorithm to complete the sparse representation of apoptosis protein sequences, using multilayer pooling based on different sizes of dictionaries to integrate the processed features, as well as oversampling approach to decrease the influences caused by unbalanced data sets. Then the extracted features were input to a support vector machine to predict the subcellular localization of the apoptosis protein. The experiment results obtained by Jackknife test on two benchmark data sets indicate that our method can significantly improve the accuracy of the apoptosis protein subcellular localization prediction.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N