Non-parametric classification of protein secondary structures

Zintzaras, E.; Brown, N. P.; Kowald, A.

doi:10.1016/j.compbiomed.2004.10.001

アイテム詳細

登録内容を編集ファイル形式で保存

一時保存へ追加

タグ情報を表示リリース履歴を表示詳細要約

公開

学術論文

Non-parametric classification of protein secondary structures

MPS-Authors

/persons/resource/persons50393

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

External Resource

There are no locators available

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

フルテキスト (公開)

Zintzaras.pdf
(全文テキスト（全般）), 369KB

付随資料 (公開)

There is no public supplementary material available

引用

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.

引用: https://hdl.handle.net/11858/00-001M-0000-0010-84B8-4

要旨

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.