Predicting potential side effects of drugs by recommender methods and ensemble learning

Neurocomputing

Volumn 173, Issue , 2016, Pages 979-987

  Zhang, Wen ^a   Zou, Hua ^a   Luo, Longqiang ^a   Liu, Qianchao ^a   Wu, Weijian ^a   Xiao, Wenyi ^a  

^a WUHAN UNIVERSITY   (China)

Author keywords

Drug side effects; Ensemble learning; Recommender system; Restricted Boltzmann machine

Indexed keywords

ARTIFICIAL INTELLIGENCE; FORECASTING; LEARNING SYSTEMS; RECOMMENDER SYSTEMS;

BETTER PERFORMANCE; DRUG SIDE EFFECTS; ENSEMBLE LEARNING; ENSEMBLE METHODS; MACHINE LEARNING METHODS; NEIGHBORHOOD-BASED METHOD; RESTRICTED BOLTZMANN MACHINE; SPECIFIC PROBLEMS;

DRUG INTERACTIONS;

ADVERSE DRUG REACTION; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER PREDICTION; DATA MINING; DRUG DATABASE; ENSEMBLE LEARNING; INFORMATION SYSTEM; INTEGRATED NEIGHBORHOOD BASED METHOD; INTERMETHOD COMPARISON; MACHINE LEARNING; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; RECOMMENDER SYSTEM; RESTRICTED BOLTZMANN MACHINE BASED METHOD;

EID: 84959346216     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.08.054     Document Type: Article

References (36)

1. Fliri A.F., Loging W.T., Thadeio P.F., Volkmann R.A. Analysis of drug-induced effect patterns to link structure and side effects of medicines. Nat. Chem. Biol. 2005, 1:389-397.
2. M. Fukuzaki, M. Seki, H. Kashima, J. Sese. Side effect prediction using cooperative pathways, in: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine BIBM'09, 2009, pp. 142-147.
3. Hammann F., Gutmann H., Vogt N., Helma C., Drewe J. Prediction of adverse drug reactions using decision tree modeling. Clin. Pharmacol. Ther. 2010, 88:52-59.
4. Bender A., Scheiber J., Glick M., Davies J.W., Azzaoui K., Hamon J., Urban L., Whitebread S., Jenkins J.L. Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure. ChemMedChem 2007, 2:861-873.
5. Huang L.C., Wu X., Chen J.Y. Predicting adverse side effects of drugs. BMC Genom. 2011, 12(Suppl 5):S11.
6. Pauwels E., Stoven V., Yamanishi Y. Predicting drug side-effect profiles: a chemical fragment-based approach. BMC Bioinform. 2011, 12:169.
7. Mizutani S., Pauwels E., Stoven V., Goto S., Yamanishi Y. Relating drug-protein interaction network with drug side effects. Bioinformatics 2012, 28:i522-i528.
8. Yamanishi Y., Pauwels E., Kotera M. Drug side-effect prediction based on the integration of chemical and biological spaces. J. Chem. Inf. Model. 2012, 52:3284-3292.
9. Liu M., Wu Y.H., Chen Y.K., Sun J.C., Zhao Z.M., Chen X.W., Matheny M.E., Xu H. Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs. J. Am. Med. Inf. Assoc. 2012, 19:E28-E35.
10. Bresso E., Grisoni R., Marchetti G., Karaboga A.S., Souchet M., Devignes M.D., Smail-Tabbone M. Integrative relational machine-learning approach for understanding drug side-effect profiles. BMC Bioinform. 2013, 14:207.
11. Huang L.C., Wu X., Chen J.Y. Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures. Proteomics 2013, 13:313-324.
12. M.J. Jahid, J. Ruan, An ensemble approach for drug side effect prediction, in: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2013, pp. 440-445.
13. Liu M., Cai R., Hu Y., Matheny M.E., Sun J., Hu J., Xu H. Determining molecular predictors of adverse drug reactions with causality analysis based on structure learning. J. Am. Med. Inform. Assoc. 2014, 21:245-251.
14. Cheng F., Li W., Wang X., Zhou Y., Wu Z., Shen J., Tang Y. Adverse drug events: database construction and in silico prediction. J. Chem. Inf. Model. 2013, 53:744-752.
15. Kuhn M., Campillos M., Letunic I., Jensen L.J., Bork P. A side effect resource to capture phenotypic effects of drugs. Mol. Syst. Biol. 2010, 6:343.
16. Wang Y., Xiao J., Suzek T.O., Zhang J., Wang J., Bryant S.H. PubChem: a public information system for analyzing bioactivities of small molecules. Nucl. Acids Res. 2009, 37:W623-W633.
17. Li Q., Cheng T., Wang Y., Bryant S.H. PubChem as a public resource for drug discovery. Drug Discov. Today 2010, 15:1052-1057.
18. Wishart D.S., Knox C., Guo A.C., Shrivastava S., Hassanali M., Stothard P., Chang Z., Woolsey J. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucl. Acids Res. 2006, 34:D668-D672.
19. Wishart D.S., Knox C., Guo A.C., Cheng D., Shrivastava S., Tzur D., Gautam B., Hassanali M. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucl. Acids Res. 2008, 36:D901-D906.
20. Knox C., Law V., Jewison T., Liu P., Ly S., Frolkis A., Pon A., Banco K., Mak C., Neveu V., Djoumbou Y., Eisner R., Guo A.C., Wishart D.S. DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucl. Acids Res. 2011, 39:D1035-D1041.
21. Law V., Knox C., Djoumbou Y., Jewison T., Guo A.C., Liu Y., Maciejewski A., Arndt D., Wilson M., Neveu V., Tang A., Gabriel G., Ly C., Adamjee S., Dame Z.T., Han B., Zhou Y., Wishart D.S. DrugBank 4.0: shedding new light on drug metabolism. Nucl. Acids Res. 2014, 42:D1091-D1097.
22. Kanehisa M., Goto S., Furumichi M., Tanabe M., Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucl. Acids Res. 2010, 38:D355-D360.
23. Bobadilla J., Ortega F., Hernando A., Gutiérrez A. Recommender systems survey. Knowl.-Based Syst. 2013, 46:109-132.
24. Herlocker J., Konstan J.A., Riedl J. An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf. Retr. 2002, 5:287-310.
25. Zhou T., Ren J., Medo M., Zhang Y.-C. Bipartite network projection and personal recommendation. Phys. Rev. E 2007, 76:046115.
26. Fernandez-Luque L., Karlsen R., Vognild L.K. Challenges and opportunities of using recommender systems for personalized health education. Stud. Health Technol. Inform. 2009, 150:903-907.
27. Rodriguez-Carrion A., Garcia-Rubio C., Campo C., Cortes-Martin A., Garcia-Lozano E., Noriega-Vivas P. Study of LZ-based location prediction and its application to transportation recommender systems. Sensors 2012, 12:7496-7517.
28. Su X., Khoshgoftaar T.M. A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 2009:4.
29. P. Smolensky, Information Processing in Dynamical Systems: Foundations of Harmony Theory, 1986.
30. R. Salakhutdinov, A. Mnih, G. Hinton, Restricted Boltzmann machines for collaborative filtering, in: Proceedings of the 24th International Conference on Machine Learning, ACM, 2007, pp. 791-798.
31. Polikar R. Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 2006, 6:21-45.
32. Liang S., Zheng D., Standley D.M., Yao B., Zacharias M., Zhang C. EPSVR and EPMeta: prediction of antigenic epitopes using support vector regression and multiple server results. BMC Bioinform. 2010, 11:381.
33. Y. Wei, J. Thompson, C. Floudas, CONCORD: a consensus method for protein secondary structure prediction via mixed integer linear optimization, in: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science, 2011, rspa20110514.
34. Lee P.F., Soo V.W. An ensemble rank learning approach for gene prioritization. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2013, 2013:3507-3510.
35. Zhang W., Xiong Y., Zhao M., Zou H., Ye X., Liu J. Prediction of conformational B-cell epitopes from 3D structures by random forests with a distance-based feature. BMC Bioinform. 2011, 12:341.
36. W. Zhang, J. Liu, Y. Xiong, M. Ke, K. Zhang, Predicting immunogenic T-cell epitopes by combining various sequence-derived features, in: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, IEEE Computer Society, Shanghai, 2013, pp. 4-9.