GasPhos: Protein Phosphorylation Site Prediction Using a New Feature Selection Approach with a GA-Aided Ant Colony System

International Journal of Molecular Sciences

卷:21

Chi-Wei Chen¹  Lan-Ying Huang²  Yen-Wei Chu²  Chia-Feng Liao²  Kai-Po Chang³ 

[1] Department of Computer Science and Engineering, National Chung-Hsing University, Taichung City 402, Taiwan;

[2] Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung City 402, Taiwan;

[3] Ph.D. Program in Medical Biotechnology, National Chung Hsing University, Taichung City 402, Taiwan;

关键词: phosphorylation;    kinase;    ant colony system;    genetic algorithms;    feature selection;   

DOI  :  10.3390/ijms21217891

来源: DOAJ

【 摘 要 】

Protein phosphorylation is one of the most important post-translational modifications, and many biological processes are related to phosphorylation, such as DNA repair, transcriptional regulation and signal transduction and, therefore, abnormal regulation of phosphorylation usually causes diseases. If we can accurately predict human phosphorylation sites, this could help to solve human diseases. Therefore, we developed a kinase-specific phosphorylation prediction system, GasPhos, and proposed a new feature selection approach, called Gas, based on the ant colony system and a genetic algorithm and used performance evaluation strategies focused on different kinases to choose the best learning model. Gas uses the mean decrease Gini index (MDGI) as a heuristic value for path selection and adopts binary transformation strategies and new state transition rules. GasPhos can predict phosphorylation sites for six kinases and showed better performance than other phosphorylation prediction tools. The disease-related phosphorylated proteins that were predicted with GasPhos are also discussed. Finally, Gas can be applied to other issues that require feature selection, which could help to improve prediction performance. GasPhos is available at http://predictor.nchu.edu.tw/GasPhos.

【 授权许可】

Unknown