Sequence-based prediction of gamma-turn types using a physicochemical property-based decision tree method

Chyn Liaw, Chun Wei Tung, Shinn Jang Ho, Shinn-Ying Ho*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The γ-turns play important roles in protein folding and molecular recognition. The prediction and analysis of γ-turn types are important for both protein structure predictions and better understanding the characteristics of different γ-turn types. This study proposed a physicochemical property-based decision tree (PPDT) method to interpretably predict γ-turn types. In addition to the good prediction performance of PPDT, three simple and human interpretable IF-THEN rules are extracted from the decision tree constructed by PPDT. The identified informative physicochemical properties and concise rules provide a simple way for discriminating and understanding γ-turn types.

Original languageEnglish
Pages (from-to)331-335
Number of pages5
JournalWorld Academy of Science, Engineering and Technology
Volume4
Issue number5
DOIs
StatePublished - 1 May 2010

Keywords

  • Classification and regression tree (CART)
  • Gamma-turn, Physicochemical properties
  • Protein secondary structure

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