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
Link To Document :
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