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Volumn 4, Issue , 2011, Pages

Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets

Author keywords

[No Author keywords available]

Indexed keywords

MYCOBACTERIUM TUBERCULOSIS;

EID: 81255128064     PISSN: None     EISSN: 17560500     Source Type: Journal    
DOI: 10.1186/1756-0500-4-504     Document Type: Article
Times cited : (44)

References (35)
  • 1
    • 80053533812 scopus 로고    scopus 로고
    • World Health Organization
    • 2010/2011 Tuberculosis Global Facts. World Health Organization, http://www.who.int/tb/publications/2010/factsheet-tb-2010.pdf
    • 2010/2011 Tuberculosis Global Facts
  • 3
    • 0028220996 scopus 로고
    • Evolution of drug-resistant tuberculosis: A tale of two species
    • 10.1073/pnas.91.7.2428 8146134
    • Evolution of drug-resistant tuberculosis: A tale of two species. Iseman DM, Proc Natl Acad Sci USA 1994 91 2428 2429 10.1073/pnas.91.7.2428 8146134
    • (1994) Proc Natl Acad Sci USA , vol.91 , pp. 2428-2429
    • Iseman, D.M.1
  • 5
    • 0032872173 scopus 로고    scopus 로고
    • How many leads from HTS? Editorial
    • DOI 10.1016/S1359-6446(99)01393-8, PII S1359644699013938
    • How many leads from HTS? Lahana R, Drug Discov Today 1999 4 447 448 10.1016/S1359-6446(99)01393-8 10481138 (Pubitemid 29460835)
    • (1999) Drug Discovery Today , vol.4 , Issue.10 , pp. 447-448
    • Lahana, R.1
  • 6
    • 0034959530 scopus 로고    scopus 로고
    • Large-scale virtual screening for discovering leads in postgenomic era
    • Large-scale virtual screening for discovering leads in postgenomic era. Waszkowycz B, Perkins TDJ, Sykes RA, Li J, IBM Syst J 2001 1 360 376
    • (2001) IBM Syst J , vol.1 , pp. 360-376
    • Waszkowycz, B.1    Perkins, T.D.J.2    Sykes, R.A.3    Li, J.4
  • 7
    • 51349131079 scopus 로고    scopus 로고
    • Machine learning for in silico virtual screening and chemical genomics: New strategies
    • 10.2174/138620708785739899 18795887
    • Machine learning for in silico virtual screening and chemical genomics: new strategies. Vert JP, Jacob L, Comb Chem High Throughput Screen 2008 11 677 685 10.2174/138620708785739899 18795887
    • (2008) Comb Chem High Throughput Screen , vol.11 , pp. 677-685
    • Vert, J.P.1    Jacob, L.2
  • 8
    • 66249086244 scopus 로고    scopus 로고
    • Machine Learning in Virtual Screening
    • 10.2174/138620709788167980 19442063
    • Machine Learning in Virtual Screening. Melville JL, Burke EK, Hirst JD, Comb Chem High Throughput Screen 2009 12 332 343 10.2174/138620709788167980 19442063
    • (2009) Comb Chem High Throughput Screen , vol.12 , pp. 332-343
    • Melville, J.L.1    Burke, E.K.2    Hirst, J.D.3
  • 9
    • 61449101715 scopus 로고    scopus 로고
    • Classification of cytochrome P450 1A2 inhibitors and noninhibitors by machine learning techniques
    • 10.1124/dmd.108.023507 19056915
    • Classification of cytochrome P450 1A2 inhibitors and noninhibitors by machine learning techniques. Vasanthanathan P, Taboureau O, Oostenbrink C, Vermeulen NP, Olsen L, Jorgensen FS, Drug Metab Dispos 2009 37 658 664 10.1124/dmd.108.023507 19056915
    • (2009) Drug Metab Dispos , vol.37 , pp. 658-664
    • Vasanthanathan, P.1    Taboureau, O.2    Oostenbrink, C.3    Vermeulen, N.P.4    Olsen, L.5    Jorgensen, F.S.6
  • 10
    • 75149183709 scopus 로고    scopus 로고
    • Virtual screening of bioassay data
    • 10.1186/1758-2946-1-21 20150999
    • Virtual screening of bioassay data. Schierz AC, J Cheminform 2009 1 21 10.1186/1758-2946-1-21 20150999
    • (2009) J Cheminform , vol.1 , pp. 21
    • Schierz, A.C.1
  • 11
    • 77957730873 scopus 로고    scopus 로고
    • Predicting Phospholipidosis Using Machine Learning
    • Predicting Phospholipidosis Using Machine Learning. Lowe R, Glen RC, Mitchell JB, Mol Pharm 2010
    • (2010) Mol Pharm
    • Lowe, R.1    Glen, R.C.2    Mitchell, J.B.3
  • 12
    • 59149091775 scopus 로고    scopus 로고
    • Weka machine learning for predicting the phospholipidosis inducing potential
    • 10.2174/156802608786786589 19075775
    • Weka machine learning for predicting the phospholipidosis inducing potential. Ivanciuc O, Curr Top Med Chem 2008 8 1691 1709 10.2174/ 156802608786786589 19075775
    • (2008) Curr Top Med Chem , vol.8 , pp. 1691-1709
    • Ivanciuc, O.1
  • 15
    • 77957680455 scopus 로고    scopus 로고
    • Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis
    • 10.1039/c0mb00104j 20835433
    • Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis. Ekins S, Kaneko T, Lipinski CA, Bradford J, Dole K, Spektor A, et al. Mol Biosyst 2010 6 2316 2324 10.1039/c0mb00104j 20835433
    • (2010) Mol Biosyst , vol.6 , pp. 2316-2324
    • Ekins, S.1    Kaneko, T.2    Lipinski, C.A.3    Bradford, J.4    Dole, K.5    Spektor, A.6
  • 16
    • 77952326917 scopus 로고    scopus 로고
    • A collaborative database and computational models for tuberculosis drug discovery
    • 10.1039/b917766c 20567770
    • A collaborative database and computational models for tuberculosis drug discovery. Ekins S, Bradford J, Dole K, Spektor A, Gregory K, Blondeau D, et al. Mol Biosyst 2010 6 840 851 10.1039/b917766c 20567770
    • (2010) Mol Biosyst , vol.6 , pp. 840-851
    • Ekins, S.1    Bradford, J.2    Dole, K.3    Spektor, A.4    Gregory, K.5    Blondeau, D.6
  • 17
    • 79961170425 scopus 로고    scopus 로고
    • Validating New Tuberculosis Computational Models with Public Whole Cell Screening Aerobic Activity Datasets
    • Validating New Tuberculosis Computational Models with Public Whole Cell Screening Aerobic Activity Datasets. Ekins S, Freundlich JS, Pharm Res 2011
    • (2011) Pharm Res
    • Ekins, S.1    Freundlich, J.S.2
  • 18
    • 35748932917 scopus 로고    scopus 로고
    • A review of feature selection techniques in bioinformatics
    • DOI 10.1093/bioinformatics/btm344
    • A review of feature selection techniques in bioinformatics. Saeys Y, Inza I, Larranaga P, Bioinformatics 2007 23 2507 2517 10.1093/bioinformatics/btm344 17720704 (Pubitemid 350048351)
    • (2007) Bioinformatics , vol.23 , Issue.19 , pp. 2507-2517
    • Saeys, Y.1    Inza, I.2    Larranaga, P.3
  • 19
    • 77955036815 scopus 로고    scopus 로고
    • Applying the Naive Bayes classifier with kernel density estimation to the prediction of protein-protein interaction sites
    • 10.1093/bioinformatics/btq302 20529890
    • Applying the Naive Bayes classifier with kernel density estimation to the prediction of protein-protein interaction sites. Murakami Y, Mizuguchi K, Bioinformatics 2010 26 1841 1848 10.1093/bioinformatics/btq302 20529890
    • (2010) Bioinformatics , vol.26 , pp. 1841-1848
    • Murakami, Y.1    Mizuguchi, K.2
  • 20
    • 67849104638 scopus 로고    scopus 로고
    • PubChem: A public information system for analyzing bioactivities of small molecules
    • 10.1093/nar/gkp456 19498078
    • PubChem: a public information system for analyzing bioactivities of small molecules. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH, Nucleic Acids Res 2009 37 623 W633 10.1093/nar/gkp456 19498078
    • (2009) Nucleic Acids Res , vol.37
    • Wang, Y.1    Xiao, J.2    Suzek, T.O.3    Zhang, J.4    Wang, J.5    Bryant, S.H.6
  • 21
    • 70449108128 scopus 로고    scopus 로고
    • Antituberculosis activity of the molecular libraries screening center network library
    • 10.1016/j.tube.2009.07.006
    • Antituberculosis activity of the molecular libraries screening center network library. Maddry JA, Ananthan S, Goldman RC, Hobrath JV, Kwong CD, Maddox C, et al. Tuberculosis (Edinb) 2009 89 354 363 10.1016/j.tube.2009.07.006
    • (2009) Tuberculosis (Edinb) , vol.89 , pp. 354-363
    • Maddry, J.A.1    Ananthan, S.2    Goldman, R.C.3    Hobrath, J.V.4    Kwong, C.D.5    Maddox, C.6
  • 24
    • 18344379900 scopus 로고    scopus 로고
    • PowerMV: A software environment for molecular viewing, descriptor generation, data analysis and hit evaluation
    • DOI 10.1021/ci049847v
    • PowerMV: a software environment for molecular viewing, descriptor generation, data analysis and hit evaluation. Liu K, Feng J, Young SS, J Chem Inf Model 2005 45 515 522 10.1021/ci049847v 15807517 (Pubitemid 40635359)
    • (2005) Journal of Chemical Information and Modeling , vol.45 , Issue.2 , pp. 515-522
    • Liu, K.1    Feng, J.2    Young, S.S.3
  • 27
    • 0031276011 scopus 로고    scopus 로고
    • Bayesian Network Classifiers
    • Bayesian Network Classifiers. Friedman N, Geiger D, GoldSzmidt M, Machine Learning 1997 29 131 163 10.1023/A:1007465528199 (Pubitemid 127510036)
    • (1997) Machine Learning , vol.29 , Issue.2-3 , pp. 131-163
    • Friedman, N.1    Geiger, D.2    Goldszmidt, M.3
  • 28
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • DOI 10.1023/A:1010933404324
    • Random Forests. Breiman L, Machine Learning 2001 45 5 32 10.1023/A:1010933404324 (Pubitemid 32933532)
    • (2001) Machine Learning , vol.45 , Issue.1 , pp. 5-32
    • Breiman, L.1
  • 32
    • 33750689152 scopus 로고    scopus 로고
    • Thresholding for making classifiers cost-sensitive
    • Proceedings of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06
    • Thresholding for Making Classifiers Cost Sensitive. Sheng VS, Ling C, Proceedings of the 21st national conference on Artificial intelligence 2006 1 476 481 (Pubitemid 44705329)
    • (2006) Proceedings of the National Conference on Artificial Intelligence , vol.1 , pp. 476-481
    • Sheng, V.S.1    Ling, C.X.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.