Commenced in January 2007
Paper Count: 30172
Sequence-based Prediction of Gamma-turn Types using a Physicochemical Property-based Decision Tree Method
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1331255Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1217
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