A Prediction Model for Membrane Proteins Using Moments Based Features.

The most expedient unit of the human body is its cell. Encapsulated within the cell are many infinitesimal entities and molecules which are protected by a cell membrane. The proteins that are associated with this lipid based bilayer cell membrane are known as membrane proteins and are considered to...

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Publicado en:BioMed Research International Vol. 2016; pp. 1 - 8
Autores principales: Butt, Ahmad Hassan, Khan, Sher Afzal, Jamil, Hamza, Rasool, Nouman, Khan, Yaser Daanial
Formato: research tables/charts Journal Article
Publicado: Wiley-Blackwell 2/15/2016
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 2/15/2016
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2016/8370132
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        atl: A Prediction Model for Membrane Proteins Using Moments Based Features.
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          Butt, Ahmad Hassan
          Khan, Sher Afzal
          Jamil, Hamza
          Rasool, Nouman
          Khan, Yaser Daanial
        affil: Department of Computer Science, School of Systems and Technology, University of Management and Technology, P.O. Box 10033, C-II, Johar Town, Lahore 54770, Pakistan
      sug:
        subj:
          Membrane Proteins Physiology
          Technology Evaluation
          Cellular Structures
      ab: The most expedient unit of the human body is its cell. Encapsulated within the cell are many infinitesimal entities and molecules which are protected by a cell membrane. The proteins that are associated with this lipid based bilayer cell membrane are known as membrane proteins and are considered to play a significant role. These membrane proteins exhibit their effect in cellular activities inside and outside of the cell. According to the scientists in pharmaceutical organizations, these membrane proteins perform key task in drug interactions. In this study, a technique is presented that is based on various computationally intelligent methods used for the prediction of membrane protein without the experimental use of mass spectrometry. Statistical moments were used to extract features and furthermore a Multilayer Neural Network was trained using backpropagation for the prediction of membrane proteins. Results show that the proposed technique performs better than existing methodologies.
      pubtype: Academic Journal
      doctype:
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        tables/charts
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      ougenre: Article
    language: English
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