English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Non-parametric classification of protein secondary structures

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.

Item is

Basic

show hide
Genre: Journal Article
Alternative Title : Comput Biol Med

Files

show Files
hide Files
:
Zintzaras.pdf (Any fulltext), 369KB
Name:
Zintzaras.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
eDoc_access: PUBLIC
License:
-

Locators

show

Creators

show
hide
 Creators:
Zintzaras, E., Author
Brown, N. P., Author
Kowald, A.1, Author              
Affiliations:
1Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433554              

Content

show
hide
Free keywords: Classification; Classification tree; Neural nets; Support vector machines; Protein structure; Physico-chemical properties; Geometrical properties; Biostatistics; Bioinformatics
 Abstract: 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.

Details

show
hide
Language(s): eng - English
 Dates: 2006-02
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 313075
DOI: 10.1016/j.compbiomed.2004.10.001
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Computers in Biology and Medicine (Elmsford, NY)
  Alternative Title : Comput Biol Med
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 36 (2) Sequence Number: - Start / End Page: 145 - 156 Identifier: ISSN: 0010-4825