de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Impressum Kontakt Einloggen
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Non-parametric classification of protein secondary structures

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50393

Kowald,  A.
Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)

Zintzaras.pdf
(beliebiger Volltext), 369KB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Zintzaras, E., Brown, N. P., & Kowald, A. (2006). Non-parametric classification of protein secondary structures. Computers in Biology and Medicine (Elmsford, NY), 36(2), 145-156. doi:10.1016/j.compbiomed.2004.10.001.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-84B8-4
Zusammenfassung
Proteins were classified into their families using a classification tree method which is based on the coefficient of variations of physico-chemical and geometrical properties of the secondary structures of proteins. The tree method uses as splitting criterion the increase in purity when a node is split into two subnodes and the size of the tree is controlled by a threshold level for the improvement of the apparent misclassification rate (AMR) of the tree after each splitting step. The classification tree method seems effective in reproducing similar structural groupings as the method of dynamic programming. For comparison, we also used another two methods: neural networks and support vector machines. We could show that the presented classification tree method performs better in classifying proteins into their families. The presented algorithm might be suitable for a rapid preliminary classification of proteins into their corresponding families.