Predicting drug side effects by multi-label learning and ensemble learning

BMC Bioinformatics

Volumn 16, Issue 1, 2015, Pages

  Zhang, Wen ^a   Liu, Feng ^a   Luo, Longqiang ^a   Zhang, Jingxia ^a  

^a WUHAN UNIVERSITY   (China)

Author keywords

Ensemble learning; Multi label learning; Side effects

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; FORECASTING; LEARNING ALGORITHMS; NEAREST NEIGHBOR SEARCH;

COMPUTATIONAL EXPERIMENT; ENSEMBLE LEARNING; INDIVIDUAL FEATURES; MACHINE LEARNING METHODS; MULTI-LABEL K-NEAREST NEIGHBORS; MULTI-LABEL LEARNING; SIDE EFFECT; STATE-OF-THE-ART METHODS;

LEARNING SYSTEMS;

ALGORITHM; AREA UNDER THE CURVE; CLUSTER ANALYSIS; DATA BASE; DRUG-RELATED SIDE EFFECTS AND ADVERSE REACTIONS; HUMAN; MACHINE LEARNING;

ALGORITHMS; AREA UNDER CURVE; CLUSTER ANALYSIS; DATABASES AS TOPIC; DRUG-RELATED SIDE EFFECTS AND ADVERSE REACTIONS; HUMANS; MACHINE LEARNING;

EID: 84946416532     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-015-0774-y     Document Type: Article

References (33)

1. Mizuno N, Niwa T, Yotsumoto Y, Sugiyama Y. Impact of drug transporter studies on drug discovery and development. Pharmacol Rev. 2003;55(3):425-61.
2. Fliri AF, Loging WT, Thadeio PF, Volkmann RA. Analysis of drug-induced effect patterns to link structure and side effects of medicines. Nat Chem Biol. 2005;1(7):389-97.
3. Merle L, Laroche ML, Dantoine T, Charmes JP. Predicting and preventing adverse drug reactions in the very old. Drugs Aging. 2005;22(5):375-92.
4. Bender A, Scheiber J, Glick M, Davies JW, Azzaoui K, Hamon J, et al. Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure. ChemMedChem. 2007;2(6):861-73.
5. Fukuzaki M, Seki M, Kashima H, Sese J. Side effect prediction using cooperative pathways. Bioinformatics and Biomedicine. 2009 BIBM'09 IEEE International Conference on: 2009. IEEE: Washington, DC. 142-147.
6. Huang LC, Wu X, Chen JY. Predicting adverse side effects of drugs. BMC Genomics. 2011;12 Suppl 5:S11.
7. Pauwels E, Stoven V, Yamanishi Y. Predicting drug side-effect profiles: a chemical fragment-based approach. BMC Bioinformatics. 2011;12:169.
8. Mizutani S, Pauwels E, Stoven V, Goto S, Yamanishi Y. Relating drug-protein interaction network with drug side effects. Bioinformatics. 2012;28(18):i522-8.
9. 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(12):3284-92.
10. Liu M, Wu YH, Chen YK, Sun JC, Zhao ZM, Chen XW, et al. Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs. J Am Med Inf Assoc. 2012;19(E1):E28-35.
11. Bresso E, Grisoni R, Marchetti G, Karaboga AS, Souchet M, Devignes MD, et al. Integrative relational Machine-Learning Approach for Understanding Drug Side-Effect Profiles. BMC Bioinformatics. 2013;14(1):207.
12. Cheng F, Li W, Wang X, Zhou Y, Wu Z, Shen J, et al. Adverse drug events: database construction and in silico prediction. J Chem Inf Model. 2013;53(4):744-52.
13. Huang LC, Wu X, Chen JY. Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures. Proteomics. 2013;13(2):313-24.
14. Liu M, Cai R, Hu Y, Matheny ME, Sun J, Hu J, et al. Determining molecular predictors of adverse drug reactions with causality analysis based on structure learning. J Am Med Inform Assoc. 2014;21(2):245-51.
15. Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P. A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol. 2010;6:343.
16. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 2009;37(Web Server issue):W623-633.
17. Li Q, Cheng T, Wang Y, Bryant SH. PubChem as a public resource for drug discovery. Drug Discov Today. 2010;15(23-24):1052-7.
18. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006;34(Database issue):D668-672.
19. Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, et al. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 2008;36(Database issue):D901-906.
20. Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, et al. DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res. 2011;39(Database issue):D1035-1041.
21. Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, et al. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 2014;42(Database issue):D1091-1097.
22. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic acids research. 2010;38(Database issue):D355-360.
23. Zhang M-L, Zhou Z-H. ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognition. 2007;40(7):2038-48.
24. Tsoumakas G, Katakis I. Multi-label Classification: an Overview. Greece: Dept of Informatics, Aristotle University of Thessaloniki; 2006.
25. Madjarov G, Kocev D, Gjorgjevikj D, Džeroski S. An extensive experimental comparison of methods for multi-label learning. Pattern Recognition. 2012;45(9):3084-104.
26. Tsoumakas G, Katakis I, Vlahavas I. Effective and efficient multilabel classification in domains with large number of labels. Proc ECML/PKDD 2008 Workshop on Mining Multidimensional Data (MMD'08): Antwerp, Belgium. 2008;30-44.
27. Hsu D, Kakade S, Langford J, Zhang T. Multi-Label Prediction via Compressed Sensing. In: NIPS: Vancouver, BC, Canada. 2009. 772-780.
28. Wong C, Li Y, Lee C, Huang CH. Ensemble learning algorithms for classification of mtDNA into haplogroups. Brief Bioinform. 2011;12(1):1-9.
29. Lee PF, Soo VW. An ensemble rank learning approach for gene prioritization. Osaka, Japan. Conf Proc IEEE Eng Med Biol Soc. 2013;2013:3507-10.
30. Yang P, Yoo PD, Fernando J, Zhou BB, Zhang Z, Zomaya AY. Sample Subset Optimization Techniques for Imbalanced and Ensemble Learning Problems in Bioinformatics Applications. IEEE transactions on cybernetics. 2014;44(3):445-455.
31. Zhang W, Niu Y, Xiong Y, Zhao M, Yu R, Liu J. Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning. PloS One. 2012;7(8):e43575.
32. Zhang W, Liu J, Xiong Y, Ke M, Zhang K. Predicting immunogenic T-cell epitopes by combining various sequence-derived features. In: IEEE International Conference on Bioinformatics and Biomedicine. Shanghai: IEEE Computer Society; 2013. p. 4-9.
33. Zhang W, Niu Y, Zou H, Luo L, Liu Q, Wu W. Accurate prediction of immunogenic T-cell epitopes from epitope sequences using the genetic algorithm-based ensemble learning. PLoS One. 2015;10(5):e0128194.