HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.

DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called...

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Publicado en:BioMed Research International Vol. 2017; pp. 1 - 11
Autores principales: Zaman, Rianon, Chowdhury, Shahana Yasmin, Rashid, Mahmood A., Sharma, Alok, Dehzangi, Abdollah, Shatabda, Swakkhar
Formato: equations & formulas research tables/charts Journal Article
Publicado: Wiley-Blackwell 11/14/2017
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 11/14/2017
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2017/4590609
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        atl: HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.
      aug:
        au:
          Zaman, Rianon
          Chowdhury, Shahana Yasmin
          Rashid, Mahmood A.
          Sharma, Alok
          Dehzangi, Abdollah
          Shatabda, Swakkhar
        affil: Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
      sug:
        subj:
          Carrier Proteins
          DNA Physiology
          Cell Physiology
          Human
          Diffusion of Innovation
          Technology
          Statistics
      ab: DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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