Drug-target protein interaction prediction based on AdaBoost algorithm.
10.7507/1001-5515.201802026
- Author:
Wanrong GU
1
,
2
;
Xianfen XIE
3
,
4
;
Yichen HE
5
;
Ziye ZHANG
6
Author Information
1. Department of Computer Science and Engineering, School of mathematics and information, South China Agricultural University, Guangzhou 510642, P.R.China
2. Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou 510006, P.R.China.
3. Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou 510006, P.R.China
4. Department of Statistics, School of Economy, Jinan University, Guangzhou 510632, P.R.China.txiexianfen2009@jnu.edu.cn.
5. Department of Computer Science and Engineering, School of mathematics and information, South China Agricultural University, Guangzhou 510642, P.R.China.
6. Department of Mathematics, School of Mathematics, South China University of Technology, Guangzhou 510660, P.R.China.
- Publication Type:Journal Article
- Keywords:
AdaBoost algorithm;
drug effect prediction;
score prediction;
target protein
- From:
Journal of Biomedical Engineering
2018;35(6):935-942
- CountryChina
- Language:Chinese
-
Abstract:
The drug-target protein interaction prediction can be used for the discovery of new drug effects. Recent studies often focus on the prediction of an independent matrix filling algorithm, which apply a single algorithm to predict the drug-target protein interaction. The single-model matrix-filling algorithms have low accuracy, so it is difficult to obtain satisfactory results in the prediction of drug-target protein interaction. AdaBoost algorithm is a strong multiple classifier combination framework, which is proved by the past researches in classification applications. The drug-target interaction prediction is a matrix filling problem. Therefore, we need to adjust the matrix filling problem to a classification problem before predicting the interaction among drug-target protein. We make full use of the AdaBoost algorithm framework to integrate several weak classifiers to improve performance and make accurate prediction of drug-target protein interaction. Experimental results based on the metric datasets show that our algorithm outperforms the other state-of-the-art approaches and classical methods in accuracy. Our algorithm can overcome the limitations of the single algorithm based on machine learning method, exploit the hidden factors better and improve the accuracy of prediction effectively.
