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Volumn 28, Issue 18, 2012, Pages

Drug target prediction using adverse event report systems: A pharmacogenomic approach

Author keywords

[No Author keywords available]

Indexed keywords

DRUG; PROTEIN;

EID: 84866446560     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bts413     Document Type: Article
Times cited : (115)

References (21)
  • 1
    • 79952394490 scopus 로고    scopus 로고
    • An algorithmic framework for predicting side-effects of drugs
    • Atias,N. and Sharan,R. (2011) An algorithmic framework for predicting side-effects of drugs. J. Comput. Biol., 18, 207-218.
    • (2011) J. Comput. Biol , vol.18 , pp. 207-218
    • Atias, N.1    Sharan, R.2
  • 2
    • 33846363963 scopus 로고    scopus 로고
    • Structure-activity relationships for in vitro and in vivo toxicity
    • Blagg,J. (2006) Structure-activity relationships for in vitro and in vivo toxicity. Annu. Rep. Med. Chem., 41, 353-368.
    • (2006) Annu. Rep. Med. Chem , vol.41 , pp. 353-368
    • Blagg, J.1
  • 3
    • 69849094133 scopus 로고    scopus 로고
    • Supervised prediction of drug-target interactions using bipartite local models
    • Bleakley,K. and Yamanishi,Y. (2009) Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics, 25, 2397-2403.
    • (2009) Bioinformatics , vol.25 , pp. 2397-2403
    • Bleakley, K.1    Yamanishi, Y.2
  • 4
    • 47249146126 scopus 로고    scopus 로고
    • Drug target identification using side-effect similarity
    • Campillos,M. et al. (2008) Drug target identification using side-effect similarity. Science, 321, 263-266.
    • (2008) Science , vol.321 , pp. 263-266
    • Campillos, M.1
  • 5
    • 11144341956 scopus 로고    scopus 로고
    • Chemical space and biology
    • Dobson,C. (2004) Chemical space and biology. Nature, 432, 824-828.
    • (2004) Nature , vol.432 , pp. 824-828
    • Dobson, C.1
  • 6
    • 38349114038 scopus 로고    scopus 로고
    • Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor
    • Faulon,J. et al. (2008) Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor. Bioinformatics, 24, 225-233.
    • (2008) Bioinformatics , vol.24 , pp. 225-233
    • Faulon, J.1
  • 7
    • 52749085437 scopus 로고    scopus 로고
    • Protein-ligand interaction prediction: an improved chemogenomics approach
    • Jacob,L. and Vert,J.-P. (2008) Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinformatics, 24, 2149-2156.
    • (2008) Bioinformatics , vol.24 , pp. 2149-2156
    • Jacob, L.1    Vert, J.-P.2
  • 8
    • 38549126643 scopus 로고    scopus 로고
    • KEGG for linking genomes to life and the environment
    • (Database issue)
    • Kanehisa,M. et al. (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res., 36(Database issue), D480-D485.
    • (2008) Nucleic Acids Res , vol.36
    • Kanehisa, M.1
  • 9
    • 33644874819 scopus 로고    scopus 로고
    • From genomics to chemical genomics: new developments in KEGG
    • (Database issue)
    • Kanehisa,M. et al. (2006) From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res., 34(Database issue), D354-357.
    • (2006) Nucleic Acids Res , vol.34
    • Kanehisa, M.1
  • 10
    • 70449634957 scopus 로고    scopus 로고
    • Predicting new molecular targets for known drugs
    • Keiser,M. et al. (2009) Predicting new molecular targets for known drugs. Nature, 462, 175-81.
    • (2009) Nature , vol.462 , pp. 175-81
    • Keiser, M.1
  • 11
    • 76149120425 scopus 로고    scopus 로고
    • A side effect resource to capture phenotypic effects of drugs
    • Kuhn,M. et al. (2010) A side effect resource to capture phenotypic effects of drugs. Mol. Syst. Biol., 6, 343.
    • (2010) Mol. Syst. Biol , vol.6 , pp. 343
    • Kuhn, M.1
  • 12
    • 23844458045 scopus 로고    scopus 로고
    • Graph kernels for molecular structure-activity relationship analysis with support vector machines
    • Mahe,P. et al. (2005) Graph kernels for molecular structure-activity relationship analysis with support vector machines. J. Chem. Inf. Model., 45, 939-951.
    • (2005) J. Chem. Inf. Model , vol.45 , pp. 939-951
    • Mahe, P.1
  • 13
    • 4444273377 scopus 로고    scopus 로고
    • Protein homology detection using string alignment kernels
    • Saigo,H. et al. (2004) Protein homology detection using string alignment kernels. Bioinformatics, 20, 1682-1689.
    • (2004) Bioinformatics , vol.20 , pp. 1682-1689
    • Saigo, H.1
  • 14
    • 80052068382 scopus 로고    scopus 로고
    • Adverse event profiles of platinum agents: data mining of the public version of the FDA adverse event reporting system, AERS, and reproducibility of clinical observations
    • Sakaeda,T. et al. (2011) Adverse event profiles of platinum agents: data mining of the public version of the FDA adverse event reporting system, AERS, and reproducibility of clinical observations. Int. J. Med. Sci., 8, 487-491.
    • (2011) Int. J. Med. Sci , vol.8 , pp. 487-491
    • Sakaeda, T.1
  • 15
    • 0034331004 scopus 로고    scopus 로고
    • Chemical genetics: ligand-based discovery of gene function
    • Stockwell,B. (2000) Chemical genetics: ligand-based discovery of gene function. Nat. Rev. Genet., 1, 116-125.
    • (2000) Nat. Rev. Genet , vol.1 , pp. 116-125
    • Stockwell, B.1
  • 16
    • 79959379936 scopus 로고    scopus 로고
    • Detecting drug interactions from adverse-event reports: interaction between paroxetine and pravastatin increases blood glucose levels
    • Tatonetti,N. et al. (2011) Detecting drug interactions from adverse-event reports: interaction between paroxetine and pravastatin increases blood glucose levels. Clin. Pharmacol. Ther., 90, 133-142.
    • (2011) Clin. Pharmacol. Ther , vol.90 , pp. 133-142
    • Tatonetti, N.1
  • 17
    • 84856805878 scopus 로고    scopus 로고
    • A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports
    • Tatonetti,N. et al. (2012) A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports. J. Am. Med. Inform. Assoc., 19, 79-85.
    • (2012) J. Am. Med. Inform. Assoc , vol.19 , pp. 79-85
    • Tatonetti, N.1
  • 18
    • 80054881553 scopus 로고    scopus 로고
    • Gaussian interaction profile kernels for predicting drug-target interaction
    • van Laarhoven,T. et al. (2011) Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics, 27, 3036-3043.
    • (2011) Bioinformatics , vol.27 , pp. 3036-3043
    • van Laarhoven, T.1
  • 19
    • 26944446576 scopus 로고    scopus 로고
    • Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development
    • Whitebread,S. et al. (2005) Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development. Drug Discov. Today, 10, 1421-1433.
    • (2005) Drug Discov. Today , vol.10 , pp. 1421-1433
    • Whitebread, S.1
  • 20
    • 46249090791 scopus 로고    scopus 로고
    • Prediction of drug-target interaction networks from the integration of chemical and genomic spaces
    • Yamanishi,Y. et al. (2008) Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics, 24, i232-i240.
    • (2008) Bioinformatics , vol.24
    • Yamanishi, Y.1
  • 21
    • 77954230951 scopus 로고    scopus 로고
    • Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework
    • Yamanishi,Y. et al. (2010) Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics, 26, i246-i254.
    • (2010) Bioinformatics , vol.26
    • Yamanishi, Y.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.