• DocumentCode
    3071480
  • Title

    Protein surface atom neighbourhoods classification

  • Author

    Cristea, P.D. ; Arsene, O. ; Tuduce, Rodica ; Nicolau, Dan

  • Author_Institution
    Bio-Med. Eng. Center, Univ. Politeh. of Bucharest, Bucharest, Romania
  • fYear
    2012
  • fDate
    20-22 Sept. 2012
  • Firstpage
    147
  • Lastpage
    150
  • Abstract
    The paper presents a classification of the protein surface atom neighbourhoods from the hydrophobicity perspective. Hydrophobicity is the property which is considered around each surface atom. The actual hydrophobicity distribution on the atoms that form an atom´s vicinity is replaced by an equivalent hydrophobicity density distribution, computed in a standardized octagonal pattern around the atom. All atoms hydrophobicity densities are clustered using K-means algorithm. A three layers neural network is trained for classification of the atoms vicinities having as many nodes in the output layers as clusters are.
  • Keywords
    biology computing; hydrophobicity; learning (artificial intelligence); neural nets; pattern classification; pattern clustering; proteins; K-means algorithm; atom vicinities classification; clustering; equivalent hydrophobicity density distribution; hydrophobicity property; neural network training; protein surface atom neighbourhoods classification; standardized octagonal pattern; three layers neural network; Accuracy; Atomic layer deposition; Clustering algorithms; Neural networks; Proteins; Training; Vectors; classification; clusterization; hydrophobicity;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Network Applications in Electrical Engineering (NEUREL), 2012 11th Symposium on
  • Conference_Location
    Belgrade
  • Print_ISBN
    978-1-4673-1569-2
  • Type

    conf

  • DOI
    10.1109/NEUREL.2012.6419994
  • Filename
    6419994