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