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Volumn 42, Issue 4, 2002, Pages 903-911

On the use of neural network ensembles in QSAR and QSPR

Author keywords

[No Author keywords available]

Indexed keywords

ENSEMBLE METHODS;

EID: 0036628567     PISSN: 00952338     EISSN: None     Source Type: Journal    
DOI: 10.1021/ci0203702     Document Type: Article
Times cited : (171)

References (50)
  • 1
    • 0025219602 scopus 로고
    • Neural networks applied to structure-activity relationships
    • Aoyama, T.; Suzuki, Y.; Ichikawa, H. Neural networks applied to structure-activity relationships. J. Med. Chem. 1990, 33, 905-908.
    • (1990) J. Med. Chem. , vol.33 , pp. 905-908
    • Aoyama, T.1    Suzuki, Y.2    Ichikawa, H.3
  • 2
    • 0026075594 scopus 로고
    • Application of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors
    • Andrea, T.A.; Kalayeh, H. Application of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors. J. Med. Chem. 1991, 34, 2824-2836.
    • (1991) J. Med. Chem. , vol.34 , pp. 2824-2836
    • Andrea, T.A.1    Kalayeh, H.2
  • 3
    • 0026738394 scopus 로고
    • Application of neural networks: Quantitative structure-activity relationships of the derivatives of 2,4-diamino-5-(substituted-benzyl) pyrimidines as DHFR inhibitors
    • So, S.-S.; Richards, W.G. Application of neural networks: quantitative structure-activity relationships of the derivatives of 2,4-diamino-5-(substituted-benzyl) pyrimidines as DHFR inhibitors. J. Med. Chem. 1992, 35, 3201-3207.
    • (1992) J. Med. Chem. , vol.35 , pp. 3201-3207
    • So, S.-S.1    Richards, W.G.2
  • 4
    • 0027145573 scopus 로고
    • A unified framework for Using neural networks to build QSARs
    • Ajay, A unified framework for Using neural networks to build QSARs. J. Med. Chem. 1993, 36, 3565-3671.
    • (1993) J. Med. Chem. , vol.36 , pp. 3565-3671
    • Ajay1
  • 5
    • 0027416552 scopus 로고
    • The use of neural networks for variable selection in QSAR
    • Wikel, J.H.; Dow, E.R. The use of neural networks for variable selection in QSAR. Bioorg. Med. Chem. Lett. 1993, 3, 645-651.
    • (1993) Bioorg. Med. Chem. Lett. , vol.3 , pp. 645-651
    • Wikel, J.H.1    Dow, E.R.2
  • 8
    • 0001225773 scopus 로고
    • Feed-forward neural networks in chemistry: Mathematical systems for classification and pattern recognition
    • Burns, J.A.; Whitesides, G.M. Feed-forward neural networks in chemistry: mathematical systems for classification and pattern recognition. Chem. Rev. 1993, 93, 2583.
    • (1993) Chem. Rev. , vol.93 , pp. 2583
    • Burns, J.A.1    Whitesides, G.M.2
  • 9
    • 0027945214 scopus 로고
    • Analysis of linear and nonlinear QSAR data using neural networks
    • Manallak, D.T.; Ellis, D.D.; Livingston, D.J. Analysis of linear and nonlinear QSAR data using neural networks. J. Med. Chem. 1994, 37, 3758-3767.
    • (1994) J. Med. Chem. , vol.37 , pp. 3758-3767
    • Manallak, D.T.1    Ellis, D.D.2    Livingston, D.J.3
  • 11
    • 0029970338 scopus 로고    scopus 로고
    • Evolutionary optimization in quantitative structure-activity relationship: An application of genetic neural networks
    • So, S.-S.; Karplus, M. Evolutionary optimization in quantitative structure-activity relationship: an application of genetic neural networks. J. Med. Chem. 1996, 39, 1521-1530.
    • (1996) J. Med. Chem. , vol.39 , pp. 1521-1530
    • So, S.-S.1    Karplus, M.2
  • 13
    • 0021733924 scopus 로고
    • Multivariate structure-activity relationships between data from a battery of biological tests and an ensemble of structure descriptors: The PLS method
    • Dunn, W.J.; Wold, S.; Edlund, U.; Hellberg, S. Multivariate structure-activity relationships between data from a battery of biological tests and an ensemble of structure descriptors: the PLS method. QSAR 1984, 3, 131-137.
    • (1984) QSAR , vol.3 , pp. 131-137
    • Dunn, W.J.1    Wold, S.2    Edlund, U.3    Hellberg, S.4
  • 15
    • 0001245212 scopus 로고    scopus 로고
    • Use of automatic relevance determination in QSAR studies using Bayesian neural networks
    • Burden, F.R.; Ford, M.G.; Whitley, D.C.; Winkler, D.A. Use of automatic relevance determination in QSAR studies using Bayesian neural networks. J. Chem. Info. Comput. Sci. 2000, 40, 1423-1430.
    • (2000) J. Chem. Info. Comput. Sci. , vol.40 , pp. 1423-1430
    • Burden, F.R.1    Ford, M.G.2    Whitley, D.C.3    Winkler, D.A.4
  • 16
    • 0029230341 scopus 로고
    • Automated descriptor selection for quantitative structure-activity relationships using generalized simulated annealing
    • Sutter, J.M.; Dixon, S.L.; Jurs, P.C. Automated descriptor selection for quantitative structure-activity relationships using generalized simulated annealing. J. Chem. Inf. Comput. Sci. 1995, 35, 77-84.
    • (1995) J. Chem. Inf. Comput. Sci. , vol.35 , pp. 77-84
    • Sutter, J.M.1    Dixon, S.L.2    Jurs, P.C.3
  • 17
    • 0028548591 scopus 로고
    • Evolutionary programming applied to the development of quantitative structure-activity relationships and quantitative structure-property relationships
    • Luke, B.T. Evolutionary programming applied to the development of quantitative structure-activity relationships and quantitative structure-property relationships. J. Chem. Inf. Comput. Sci. 1994, 34, 1279-1287.
    • (1994) J. Chem. Inf. Comput. Sci. , vol.34 , pp. 1279-1287
    • Luke, B.T.1
  • 18
    • 0028046665 scopus 로고
    • Variable selection in QSAR studies. I. An evolutionary algorithm
    • Kubinyi, H. Variable selection in QSAR studies. I. An evolutionary algorithm. QSAR 1994, 13, 285-294.
    • (1994) QSAR , vol.13 , pp. 285-294
    • Kubinyi, H.1
  • 19
    • 0028467707 scopus 로고
    • Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships
    • Rogers, D.R.; Hopfinger, A.J. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J. Chem. Inf. Comput. Sci. 1994, 34, 854-866.
    • (1994) J. Chem. Inf. Comput. Sci. , vol.34 , pp. 854-866
    • Rogers, D.R.1    Hopfinger, A.J.2
  • 20
    • 0035438386 scopus 로고    scopus 로고
    • Toward an optimal procedure for variable selection and QSAR model building
    • Yasri, A.; Hartsough, D. Toward an optimal procedure for variable selection and QSAR model building. J. Chem. Inf. Comput. Sci. 2001, 41, 1218-1227.
    • (2001) J. Chem. Inf. Comput. Sci. , vol.41 , pp. 1218-1227
    • Yasri, A.1    Hartsough, D.2
  • 21
    • 0031084988 scopus 로고    scopus 로고
    • GA strategy for variable selection in QSAR studies: GA-based PLS analysis of calcium channel antagonists
    • Hasegawa, K.; Miyashita, Y.; Funatsu, K. GA strategy for variable selection in QSAR studies: GA-based PLS analysis of calcium channel antagonists. J. Chem. Inf. Comput. Sci. 1997, 37, 306-310.
    • (1997) J. Chem. Inf. Comput. Sci. , vol.37 , pp. 306-310
    • Hasegawa, K.1    Miyashita, Y.2    Funatsu, K.3
  • 22
    • 0035227873 scopus 로고    scopus 로고
    • A novel method for building regression tree models for QSAR based on artificial ant colony systems
    • Izrailev, S.; Agrafiotis, D.K. A novel method for building regression tree models for QSAR based on artificial ant colony systems. J. Chem. Inf. Comput. Sci. 2001, 41, 176-180.
    • (2001) J. Chem. Inf. Comput. Sci. , vol.41 , pp. 176-180
    • Izrailev, S.1    Agrafiotis, D.K.2
  • 23
    • 0036581948 scopus 로고    scopus 로고
    • Variable selection for QSAR by artificial ant colony systems
    • Izrailev, S.; Agrafiotis, D.K. Variable selection for QSAR by artificial ant colony systems. SAR QSAR Environ. Res. 2002, 13, 417-423.
    • (2002) SAR QSAR Environ. Res. , vol.13 , pp. 417-423
    • Izrailev, S.1    Agrafiotis, D.K.2
  • 24
    • 0037186503 scopus 로고    scopus 로고
    • Feature selection for structure-activity correlation using binary particle swarms
    • Agrafiotis, D.K.; Cedeno, W. Feature selection for structure-activity correlation using binary particle swarms. J. Med. Chem. 2002, 45, 1098-1107.
    • (2002) J. Med. Chem. , vol.45 , pp. 1098-1107
    • Agrafiotis, D.K.1    Cedeno, W.2
  • 26
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman, L. Bagging predictors. Machine Learning 1996, 24, 123-140.
    • (1996) Machine Learning , vol.24 , pp. 123-140
    • Breiman, L.1
  • 28
    • 0000459353 scopus 로고    scopus 로고
    • Stacked generalization
    • Wolpert, D.H. Stacked generalization. Neural Networks 1996, 8, 1341-1390.
    • (1996) Neural Networks , vol.8 , pp. 1341-1390
    • Wolpert, D.H.1
  • 29
    • 0030196364 scopus 로고    scopus 로고
    • Stacked regressions
    • Breiman, L. Stacked regressions. Machine Learning 1996, 24, 49-64.
    • (1996) Machine Learning , vol.24 , pp. 49-64
    • Breiman, L.1
  • 30
    • 0004096460 scopus 로고    scopus 로고
    • The NeuralBag algorihm: Optimizing generalization performance in bagged neural networks
    • Verleysen, M., Ed.; Brussels, Belgium
    • Carney, J.G.; Cunningham, P. The NeuralBag algorihm: optimizing generalization performance in bagged neural networks. In Proceedings of the 7th European Symposium on Neural Networks; Verleysen, M., Ed.; Brussels, Belgium, 1997; pp 35-40.
    • (1997) Proceedings of the 7th European Symposium on Neural Networks , pp. 35-40
    • Carney, J.G.1    Cunningham, P.2
  • 31
    • 0001504093 scopus 로고    scopus 로고
    • Balancing between bagging and bumping
    • Mozer, M., Jordan, M., Petsche, T., Eds.; MIT Press
    • Heskes, T. Balancing between bagging and bumping. In Advances in Neural Information Processing Systems; Mozer, M., Jordan, M., Petsche, T., Eds.; MIT Press: 1997; Vol. 9, pp 176-182.
    • (1997) Advances in Neural Information Processing Systems , vol.9 , pp. 176-182
    • Heskes, T.1
  • 32
    • 0032928495 scopus 로고    scopus 로고
    • Developing robust nonlinear models through bootstrap aggregated neural networks
    • Zhang, J. Developing robust nonlinear models through bootstrap aggregated neural networks. Neurocomputing 1999, 25, 93-113.
    • (1999) Neurocomputing , vol.25 , pp. 93-113
    • Zhang, J.1
  • 34
    • 0013228784 scopus 로고    scopus 로고
    • Technical Report 577; Statistics Department, University of California: Berkeley, CA
    • Breiman, L. Some infinity theory fr predictor ensembles; Technical Report 577; Statistics Department, University of California: Berkeley, CA, 2000.
    • (2000) Some infinity theory fr predictor ensembles
    • Breiman, L.1
  • 35
    • 0003619255 scopus 로고    scopus 로고
    • Technical Report 460; Statistics Department, University of California: Berkeley, CA
    • Breiman, L. Bias, variance and arcing classifiers; Technical Report 460; Statistics Department, University of California: Berkeley, CA, 1996.
    • (1996) Bias, variance and arcing classifiers
    • Breiman, L.1
  • 36
    • 0032645080 scopus 로고    scopus 로고
    • An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
    • Bauer, E.; Kohavi, R. An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learning 1999, 36, 105-139.
    • (1999) Machine Learning , vol.36 , pp. 105-139
    • Bauer, E.1    Kohavi, R.2
  • 40
    • 0025490985 scopus 로고
    • Networks for approximation and learning
    • Poggio, T.; Girosi, F. Networks for approximation and learning. Proc. IEEE 1990, 78, 1481-1497.
    • (1990) Proc. IEEE , vol.78 , pp. 1481-1497
    • Poggio, T.1    Girosi, F.2
  • 41
    • 0033549850 scopus 로고    scopus 로고
    • Robust QSAR models using Bayesian regularized neural networks
    • Burden, F.R.; Winkler, D.A. Robust QSAR models using Bayesian regularized neural networks. J. Med. Chem. 1999, 42, 3183-3187.
    • (1999) J. Med. Chem. , vol.42 , pp. 3183-3187
    • Burden, F.R.1    Winkler, D.A.2
  • 46
    • 0035899180 scopus 로고    scopus 로고
    • Adaptive neuro-fuzzy inference system: An instant and architecture-free predictor for improved QSAR studies
    • Loukas, Y.L. Adaptive neuro-fuzzy inference system: an instant and architecture-free predictor for improved QSAR studies. J. Med. Chem. 2001, 44, 2772-2783.
    • (2001) J. Med. Chem. , vol.44 , pp. 2772-2783
    • Loukas, Y.L.1
  • 47
    • 85066770889 scopus 로고    scopus 로고
    • United States Patents 5, 463, 564, 1995; 5, 574, 656, 1996; 5, 684, 711, 1997; and 5, 901, 069, 1999
    • Agrafiotis, D.K.; Bone, R.F.; Salemme, F.R.; Soll, R.M. United States Patents 5, 463, 564, 1995; 5, 574, 656, 1996; 5, 684, 711, 1997; and 5, 901, 069, 1999.
    • Agrafiotis, D.K.1    Bone, R.F.2    Salemme, F.R.3    Soll, R.M.4
  • 48
    • 85066771278 scopus 로고    scopus 로고
    • Copyright 3-Dimensional Pharmaceuticals, Inc., 1994-2000
    • Copyright 3-Dimensional Pharmaceuticals, Inc., 1994-2000.
  • 49
    • 0000551189 scopus 로고    scopus 로고
    • Popular ensemble methods: An empirical study
    • Opitz, D.; Maclin, R. Popular ensemble methods: an empirical study. J. Artific. Intell. Res. 1999, 11, 169-198.
    • (1999) J. Artific. Intell. Res. , vol.11 , pp. 169-198
    • Opitz, D.1    Maclin, R.2
  • 50
    • 85054435084 scopus 로고
    • Neural network ensembles, cross-validation and active learning
    • Tesauro, G., Touretzky, D., Lean, T., Eds.; MIT Press
    • Krogh, A.; Vedelsby, J. Neural network ensembles, cross-validation and active learning. In Advances in Neural Information Processing Systems 7; Tesauro, G., Touretzky, D., Lean, T., Eds.; MIT Press: 1995; pp 231-238.
    • (1995) Advances in Neural Information Processing Systems , vol.7 , pp. 231-238
    • Krogh, A.1    Vedelsby, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.