iSucc-PseAAC:Prediction of Lysine Succinylation Modification Sites Based on Ensemble Machine Learning
10.13865/j.cnki.cjbmb.2022.04.1670
- Author:
Xin WEI
1
;
Chun-Sheng LIU
1
;
Jian-Hua JIA
2
;
Gen-Qiang WU
2
Author Information
1. Intelligent Logistics Teaching and Research Office, Business School of Jiangxi Institute of Fashion Technology
2. Bioinformatics Research Laboratory, School of Information Engineering, Jingdezhen Ceramic Institute
- Publication Type:Journal Article
- Keywords:
ensemble learning;
feature extraction;
machine learning;
succinylation
- From:
Chinese Journal of Biochemistry and Molecular Biology
2022;38(6):816-822
- CountryChina
- Language:Chinese
-
Abstract:
Lysine succinylation is a novel post-translational modification, which plays an important role in regulating distinct cellular functions control, therefore it is necessary to accurately identify succinylation sites in proteins. As traditional experiments consume material and financial resources, prediction by calculation is an efficient method being proposed recently. In this study, we developed a new prediction method iSucc-PseAAC, which uses a variety of classification algorithms combined with different feature extraction methods. Moreover, it is found that under the feature extraction based on coupled sequence (PseAAC), the classification effect of support vector machine is the best, and it could be combined with ensemble learning to solve the problem of data imbalance. Compared with the existing methods, iSucc-PseAAC has more significance and practicality in predicting lysine succinylation sites.
