Machine Learning Methods for the Protein Fold Recognition Problem

2018 ◽  
pp. 101-127 ◽  
Author(s):  
Katarzyna Stapor ◽  
Irena Roterman-Konieczna ◽  
Piotr Fabian
Keyword(s):  
Machine Learning ◽  
Fold Recognition ◽  
Recognition Problem ◽  
Protein Fold ◽  
Learning Methods ◽  
Protein Fold Recognition ◽  
Machine Learning Methods

A Genetic Threading Method with Combined Structure- and Sequence-Based Information

2013 ◽  
Vol 634-638 ◽  
pp. 3930-3935
Author(s):  
Jun Feng Gu ◽  
Xi Cheng Wang
Keyword(s):  
Genetic Algorithm ◽  
Fold Recognition ◽  
Energy Model ◽  
Alignment Accuracy ◽  
Recognition Problem ◽  
Protein Fold ◽  
Genetic Operators ◽  
Protein Fold Recognition ◽  
Recognition Ability ◽  
Combined Structure

A threading program based on genetic algorithm, is proposed for the protein fold recognition problem. Proper algorithm is designed in which genetic operators can be effectively implemented, and a more realistic energy model is adopted in this work. The performance of the genetic threading method is tested on a standard fold recognition benchmark, and the results show that the genetic threading method has good alignment accuracy and fold recognition ability. The analysis of the results demonstrates the rationality of our energy model.

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