Drug-target protein interaction prediction based on AdaBoost algorithm_Journal of Biomedical Engineering

Authors：

GU Wanrong ^1,2 ,  XIE Xianfen ^2,3 , HE Yichen ¹ , ZHANG Ziye ⁴

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. Department of Statistics, School of Economy, Jinan University, Guangzhou 510632, P.R.China;
4. Department of Mathematics, School of Mathematics, South China University of Technology, Guangzhou 510660, P.R.China;

Corresponding author：

XIE Xianfen, Email: txiexianfen2009@jnu.edu.cn

Keywords：

target protein; drug effect prediction; score prediction; AdaBoost algorithm

DOI：

10.7507/1001-5515.201802026

Video：

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Abstract Full text Figures/Tables Video References Cited by

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.

Citation： GU Wanrong, XIE Xianfen, HE Yichen, ZHANG Ziye. Drug-target protein interaction prediction based on AdaBoost algorithm. Journal of Biomedical Engineering, 2018, 35(6): 935-942. doi: 10.7507/1001-5515.201802026 Copy

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16.	Schapire R E. The strength of weak learnability. Mach Learn, 1990, 5(2): 197-227.
17.	Bleakley K, Yamanishi Y. Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics, 2009, 25(18): 2397-2403.
18.	Takács D, Pilászy I, Németh B. Major components of the gravity recommendation system. ACM SIGKDD Explorations Newsletter, 2007, 9(2): 80-83.
19.	Wu Zhenhua, Chen Xiaosu, Xiao Daoju. Offline Chinese signature verification based on segmentation and RBFNN classifier. Acta Automatica Sinica, 2007, 345(1): 995-1001.
20.	Li Dongsheng, Chen Chao, Lv Qin, et al. An algorithm for efficient privacy-preserving item-based collaborative filtering. Future Generation Computer Systems, 2016, 55(C): 311-320.
21.	Bilal M, Israr H, Shahid M, et al. Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques. Journal of King Saud University-Computer and Information Sciences, 2016, 28(3): 330-344.
22.	Patel T B, Patil H A. Cochlear filter and instantaneous frequency based features for spoofed speech detection. IEEE J Sel Top Signal Process, 2017, 11(4): 618-631.
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1. Paul S M, Mytelka D S, Dunwiddie C T, et al. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat Rev Drug Discov, 2010, 9(3): 203-214.
2. Law V, Knox C, Djoumbou Y, et al. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Research, 2014, 42(Database issue): D1091-D1097.
3. Kuhn M, Szklarczyk D, Pletscher-Frankild S A, et al. STITCH 4: integration of protein-chemical interactions with user data. Nucleic Acids Res, 2014, 42(D1): D401-D407.
4. Chen Xing, Liu Mingxi, Yan G Y. Drug-target interaction prediction by random walk on the heterogeneous network. Mol Biosyst, 2012, 8(7): 1970-1978.
5. Iskar M, Zeller G, Zhao Xingming, et al. Drug discovery in the age of systems biology: the rise of computational approaches for data integration. Current opinion in biotechnology, 2012, 23(4): 609-616.
6. Hopkins A L, Groom C R. The druggable genome. Nature reviews Drug discovery, 2002, 1: 727-730.
7. Drews J. Drug discovery: A historical perspective. Science, 2000, 287(5460): 1960-1964.
8. Overington J P, Al-Lazikani B, Hopkins A L. How many drug targets are there? Nature Reviews Drug discovery, 2006, 5: 993-996.
9. Landry Y, Gies J P. Drugs and their molecular targets: an updated overview. Fundamental & clinical pharmacology, 2008, 22(1): 1-18.
10. Zhu Ji, Zou Hui, Rosset S, et al. Multi-class AdaBoost. Stat Interface, 2009, 2(3): 349-360.
11. Campillos M, Kuhn M, Gavin A C, et al. Drug target identification using side-effect similarity. Science, 2008, 321(5886): 263-266.
12. 周福家, 张宏伟, 李卫国. 分子网络多靶标筛选的粒子群数值模拟法. 计算力学学报, 2015, 32(2): 269-273.
13. Yamanishi Y, Araki M, Gutteridge A A, et al. Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics, 2008, 24(13): I232-I240.
14. Keiser M J, Setola V, Irwin J J, et al. Predicting new molecular targets for known drugs. Nature, 2009, 462(7270): 175-181.
15. Cobanoglu M C, Liu Chang, Hu Feizhuo, et al. Predicting drug-target interactions using probabilistic matrix factorization. J Chem Inf Model, 2013, 53(12): 3399-3409.
16. Schapire R E. The strength of weak learnability. Mach Learn, 1990, 5(2): 197-227.
17. Bleakley K, Yamanishi Y. Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics, 2009, 25(18): 2397-2403.
18. Takács D, Pilászy I, Németh B. Major components of the gravity recommendation system. ACM SIGKDD Explorations Newsletter, 2007, 9(2): 80-83.
19. Wu Zhenhua, Chen Xiaosu, Xiao Daoju. Offline Chinese signature verification based on segmentation and RBFNN classifier. Acta Automatica Sinica, 2007, 345(1): 995-1001.
20. Li Dongsheng, Chen Chao, Lv Qin, et al. An algorithm for efficient privacy-preserving item-based collaborative filtering. Future Generation Computer Systems, 2016, 55(C): 311-320.
21. Bilal M, Israr H, Shahid M, et al. Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques. Journal of King Saud University-Computer and Information Sciences, 2016, 28(3): 330-344.
22. Patel T B, Patil H A. Cochlear filter and instantaneous frequency based features for spoofed speech detection. IEEE J Sel Top Signal Process, 2017, 11(4): 618-631.
23. 刘晓峰, 张雪英, Wang Z J. Logistic核函数及其在语音识别中的应用. 华南理工大学学报:自然科学版, 2015, 43(5): 100-106.

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