化学データにおける線形・非線形モデリング
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| Published in | CICSJ Bulletin Vol. 23; no. 1; p. 13 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | Japanese |
| Published |
公益社団法人 日本化学会・情報化学部会
2005
Division of Chemical Information and Computer Sciences The Chemical Society of Japan |
| Online Access | Get full text |
| ISSN | 0913-3747 1347-2283 |
| DOI | 10.11546/cicsj.23.13 |
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| Author | 金谷, 重彦 Altaf-Ul-Amin, Md 黒川, 顕 |
|---|---|
| Author_FL | Md. Altaf-Ul-Amin 金谷 重彦 黒川 顕 |
| Author_FL_xml | – sequence: 1 fullname: 金谷 重彦 – sequence: 2 fullname: Md. Altaf-Ul-Amin – sequence: 3 fullname: 黒川 顕 |
| Author_xml | – sequence: 1 fullname: 黒川, 顕 organization: 奈良先端科学技術大学院大学・情報科学研究科 – sequence: 1 fullname: 金谷, 重彦 organization: 奈良先端科学技術大学院大学・情報科学研究科 – sequence: 1 fullname: Altaf-Ul-Amin, Md organization: 奈良先端科学技術大学院大学・情報科学研究科 |
| BackLink | https://cir.nii.ac.jp/crid/1390001204366813312$$DView record in CiNii |
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| Copyright | 2005 日本化学会 |
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| Publisher | 公益社団法人 日本化学会・情報化学部会 Division of Chemical Information and Computer Sciences The Chemical Society of Japan |
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| References | Kanaya, S., et al., (1996) Detection of genes in Escherichia coli sequences determined by genome projects and prediction of protein production levels, based on multivariate diversity in codon usage. CABIOS (renamed as Bioinformatics), 12, 213-225. Hasegawa, K. et al., (2001) Nonliniear modeling a structure-activity data by combining genetic algorithms and counter propagation neural networks. J. Comput. Aided Chem., 2, 11-20. Levine, B. K. et al., (2002) Chemometrics, Anal. Chem. 74, 2764-9. Shepard, R. N., (1962) The analysis of proximities: mutldimensional scaling with an unknown distance function. II. Psychometrika, 27, 219-246. Kratochiwil N. A., Huber. W., Muller, F., Kansy, M. Gerber P. R. (2002) Predicting plasma protein binding of drugs: a new approach. Biochem. Pharmacol., 64, 1355-74. Beger, R. et al., (2002) Comparative structural connectivity spectra analysis models of steroid binding to the corticosteroid binding globulin. J. Chem. Inf. Comput. Sci. 42, 1123-1131. Arakawa, M. et al., (2000) Selection of bioactive conformations and alignment rules by 4way PLS analysis: Validation using 3D-structure of protein. J. Comput. Aided Chem. 1, 1-7. Takagi, T. et al, (2003) A new multiple comparison method using resampling technique for medical, pharmaceutical, and chemical data. J. Comput. Aided Chem., 4, 27-34. McGovern A. et al., (2002) Monitoring of complex industrial bioprocesses for metabolite concentration using modern spectroscopies nad machine learning: application to gibberellic acid production. Biotechnol. Bioeng., 78, 527-38. Miyashita, Y. et al., (1986) Computer-assisted structure/tasete studies on sulfamates by pattern recognition methods. Anal. Chim. Acta, 184, 143-149. Dinc E, et al., (2002) Spectrophotometric quantitative determination of cilazapril and hydrochlorothiazide in tablets by chemometric methods. J. Pharm. Biomed. Anal., 30, 715-23. Kirew D.B., et al., (1998) Application of Kohonen Neural Networks in classification of biological active compounds. SAR QSAR Environ. Res., 8, 93-107. PCA (Principal component analysis) SOM (Self-organizing maps)Kohonen, T., (1982) Self-organized formation of topologically correct feature maps. Biol. Cybern., 43, 59-69. Kimura, T., et al., (1996) Quantitative structure-activity relationships of the synthetic substrates for elastase enzyme using nonlinear partial least squares regression. J. Chem. Inf. Comput. Sci., 36, 185-9. Tamayo, P., et al., (1999) Interpreting patterns of gene expression with self-organizing maps, Proc. Natl. Acad. Sci. USA, 96, 2907-2912. Marcia, R., et al., (2001) Time course of LPS-induced gene expression in a mouse model of genitourinary inflammation, Physiol. Genomics, 5, 147-160. Shen, Q. et al., (2003) Quantitative structure-activity relationships (QSAR): studies of inhibitors of tyrosine kinase. Eur. J. Pharm. Sci., 20, 63-71. Pascual-Montano, A. et al., (2002) Quantitative self-organizing maps for clustering electron tomograms. J. Struct. Biol., 138, 114-22. Carlsen L., et al., (2002) QSAR’s based on partial order ranking, SAR QSAR Environ Res., 13, 153-65. Miyasyita, Y., et al., (1992) Multivariate structure activity relationships analysis of fungicidal and herbicidal thiolcarbamates using partial least squares method, Quant.Struct. Act. Relat., 11, 17-22. Tanada, T. et al., (2000) Development of material design program based on chemometrics. J. Comput. Aided Chem., 1, 35-46. Vang Ol et al., (2001) Biochemical effects of dietary intake of different broccoli samples. II. Metabolism, 50, 113-5. Chemometricsに関するチュートリアルページが以下のサイトで公開されている。このサイトは必見 Lindberg, W., et al., (1983) Partial least-squares method for spectrofluorimetric analysis of mixtures of humic acid and liginsulfonate, Anal. Chem., 55, 643-648. Pintore, M., et al., (2001) Database mining applied to central nervous system (CNS) activity. Eur. J. Med. Chem., 36, 349-59. Urata S. et al. (2001) Application of new ensemble learning method for the reguression analysis: arcing_RA. J. Comput. Aided Chem., 2, 70-78. Massart, D. L. et al., (1988). "Chemometrics: a text book", Elsevier Amsterdam. Kanaya, S. et al., (1999) Studies of codon usage and tRNA genes of 18 unicellular organisms and quantification of Bacillus subtilis tRNAs, Gene, 238, 143-155. Wold, S., et al., (1989) Nonlinear PLS modeling, Chemom. Intell. Lab. Systems, 7, 53-65. Arakawa, M. et al., (2000) Selection of bioinactive conformations and alignment rules by 4 way PLS analysis: Validation using external data. J. Comput. Aided Chem., 1, 8-14. Wold S. (1991) Chemometrics, why, what and where to next? J. Pharm. Biomed. Anal., 9, 589-96. Brown, S. et al., (1992) Chemometrics, Anal. Chem., 64, 22R-49R. Dutta, R., et al., (2003) Electronic nose based tea quality standardization. Neural Networks, 16, 847-853. Ressom H. et al., (2003) Clustering gene expression data using adaptive double self-organizing map. Neural Networks, 14, 35-46. MDS(Multidimensional scaling) HNN (Hopfield Neural Networks) Tanada, T. et al., (2000) Development of material design program based on chemometrics. J Comput. Aided Chem., 1, 35-46. Geladi, P., et al., (1986) Partial least-squares regression: a tutorial, Anal. Chim. Acta, 189, 1-17. Polanski, J., Walczak, B. (2000) The comparative molecular surface analysis (COMSA): a novel tool for molecular design. Comput. and Chem., 24, 615-25. Hu, S. Y. et al., (2000) Partial least square analysis of lysozyme near-infrared spectra. Appl. Biochem. Biotechnol., 87, 153-63. Arakawa, M. et al., (2003) Multi-way PLS modeling of structure-activity data by incorporating electrostatic and lipophilic potentials on molecular surface. J. Comput. Aided Chem., 4, 18-26. Arakawa, M., et al. (2002) Application of novel molecular alignment method using Hopfield Neural Network to 3D-QSAR: Analysis of HER2 inhibitors. 3, 99-106. Hiltunen, H. et al., (2001) Application of self-organizing maps in conformational analysis of lipids. J. Am. Chem. Soc., 123, 810-6. Zheng, G., Huang W. H., Lu, X. H., (2003) Prediction of n-octanol/water partition coefficients for polychlorinated dibenzo-p-dioxins using a general regression neural network. Anal. Bioanal. Chem. 376, 680-5. Abe, T., et al., (2003) Informatics for unveiling hidden genome signatures. Genome Res., 13, 693-702. Hasegawa, K., et al., J. (2002) Data mining of structure-activity data through genetic algorithm and counter propagation neural network. Computer Aided Chem., 3, 90-98. Bleharski J. R., et al., Use of genetic profiling in leprosy to discriminate clinical forms of the disease. Sci., 301, 1527-30. Eddy C. V. and Arnold M. A. (2001) Near-infrared spectroscopy for measuring urea in hemodialysis fluids. Clin. Chem., 47, 1279-86. Hasegawa, K.,et al., (2001) Nonlinear modeling of structure-activity data by combining genetic algorithms and counter propagation neural networks. J. Computer Aided Chem., 2, 11-20. Nishio, H., et al., (2003) Visualization of gene classification based on expression profile using BL-SOM. Proceedings of Workshop on Self-organizing maps, 1-6. PLS (Partial Least Squares) Purohit P. V., Rocke, D. M., (2003) Discriminant models for high-throughput proteomics mass spectrometer data. Proteomics, 3, 1699-703. Kowalski, B., (1975) Chemometrics, J. Chem. Inf. Comput. Sci., 15, 201-203 Arakawa, M. et al., (2002) Application of novel molecular alignment method using Hopfield Neural Network to 3D-QSAR. J. Computer Aided Chem., 3, 63-72. Shaw A.D. et al., (2000) Rapid analysis of high-dimensional bioprocesses using multivariate spectroscopies and advanced chemometrics. Adv. Biochem.Eng.Biotechnol., 66, 83-113. GA (Genetic algorithm)Nakayama, S., et al., (2000) A similarity score of protein three dimensional structures by hard ball model using a genetic algorithm. J. Comput. Aided Chem., 1, 15-21. Stanton, D. T. (2003) On the physical interpretation of QSAR models., J. Chem. Inf. Comput. Sci., 43, 1423-33. Urata, S. et al. (2001) Application of artificial neural network to Hirai, M.Y. et al, (2004) Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana, Proc. Natl. Acad. Sci. USA, 101,10205-10210. Takagi T. et al, (2002) The comparison of generalized additive model with artificial hierarchical neural network in the analysis of pharmaceutical data. J. Comput. Aided Chem., 3, 56-62. Igor V. T. et al., (2001) Volume learning algorithm artificial neural networks for 3D QSAR studies. J. Med. Chem., 44, 2411-2420. Gregory A. B. et al., (2001) QSARs for 6-azasteroids as inhibitors of human type 1 5?-reductase. J. Chem. Inf. Coput. Sci., 41, 1255-1265. Kihara, M. et al., (2002) Chemical information analysis of coal parameter. J. Comput. aided Chem. 3, 1-7. Matsuoka S., et al., (2000) Development of new 3D-QSAR method by Kohonen network and 3 way PLS analysis. J. Comput. Aided Chem., 1, 22-34. Schneider, G., (1999) How many potentially secreted proteins are contained in a bacterial genome? Gene, 327, 113-21. |
| References_xml | – reference: McGovern A. et al., (2002) Monitoring of complex industrial bioprocesses for metabolite concentration using modern spectroscopies nad machine learning: application to gibberellic acid production. Biotechnol. Bioeng., 78, 527-38. – reference: Dinc E, et al., (2002) Spectrophotometric quantitative determination of cilazapril and hydrochlorothiazide in tablets by chemometric methods. J. Pharm. Biomed. Anal., 30, 715-23. – reference: GA (Genetic algorithm)Nakayama, S., et al., (2000) A similarity score of protein three dimensional structures by hard ball model using a genetic algorithm. J. Comput. Aided Chem., 1, 15-21. – reference: Takagi, T. et al, (2003) A new multiple comparison method using resampling technique for medical, pharmaceutical, and chemical data. J. Comput. Aided Chem., 4, 27-34. – reference: Tanada, T. et al., (2000) Development of material design program based on chemometrics. J Comput. Aided Chem., 1, 35-46. – reference: Wold, S., et al., (1989) Nonlinear PLS modeling, Chemom. Intell. Lab. Systems, 7, 53-65. – reference: Hasegawa, K.,et al., (2001) Nonlinear modeling of structure-activity data by combining genetic algorithms and counter propagation neural networks. J. Computer Aided Chem., 2, 11-20. – reference: Levine, B. K. et al., (2002) Chemometrics, Anal. Chem. 74, 2764-9. – reference: Miyasyita, Y., et al., (1992) Multivariate structure activity relationships analysis of fungicidal and herbicidal thiolcarbamates using partial least squares method, Quant.Struct. Act. Relat., 11, 17-22. – reference: Kimura, T., et al., (1996) Quantitative structure-activity relationships of the synthetic substrates for elastase enzyme using nonlinear partial least squares regression. J. Chem. Inf. Comput. Sci., 36, 185-9. – reference: Carlsen L., et al., (2002) QSAR’s based on partial order ranking, SAR QSAR Environ Res., 13, 153-65. – reference: Urata, S. et al. (2001) Application of artificial neural network to – reference: Takagi T. et al, (2002) The comparison of generalized additive model with artificial hierarchical neural network in the analysis of pharmaceutical data. J. Comput. Aided Chem., 3, 56-62. – reference: Arakawa, M. et al., (2000) Selection of bioinactive conformations and alignment rules by 4 way PLS analysis: Validation using external data. J. Comput. Aided Chem., 1, 8-14. – reference: Kanaya, S. et al., (1999) Studies of codon usage and tRNA genes of 18 unicellular organisms and quantification of Bacillus subtilis tRNAs, Gene, 238, 143-155. – reference: Eddy C. V. and Arnold M. A. (2001) Near-infrared spectroscopy for measuring urea in hemodialysis fluids. Clin. Chem., 47, 1279-86. – reference: Beger, R. et al., (2002) Comparative structural connectivity spectra analysis models of steroid binding to the corticosteroid binding globulin. J. Chem. Inf. Comput. Sci. 42, 1123-1131. – reference: Urata S. et al. (2001) Application of new ensemble learning method for the reguression analysis: arcing_RA. J. Comput. Aided Chem., 2, 70-78. – reference: HNN (Hopfield Neural Networks) – reference: Hasegawa, K., et al., J. (2002) Data mining of structure-activity data through genetic algorithm and counter propagation neural network. Computer Aided Chem., 3, 90-98. – reference: SOM (Self-organizing maps)Kohonen, T., (1982) Self-organized formation of topologically correct feature maps. Biol. Cybern., 43, 59-69. – reference: Miyashita, Y. et al., (1986) Computer-assisted structure/tasete studies on sulfamates by pattern recognition methods. Anal. Chim. Acta, 184, 143-149. – reference: Kanaya, S., et al., (1996) Detection of genes in Escherichia coli sequences determined by genome projects and prediction of protein production levels, based on multivariate diversity in codon usage. CABIOS (renamed as Bioinformatics), 12, 213-225. – reference: MDS(Multidimensional scaling) – reference: Geladi, P., et al., (1986) Partial least-squares regression: a tutorial, Anal. Chim. Acta, 189, 1-17. – reference: Vang Ol et al., (2001) Biochemical effects of dietary intake of different broccoli samples. II. Metabolism, 50, 113-5. – reference: Arakawa, M., et al. (2002) Application of novel molecular alignment method using Hopfield Neural Network to 3D-QSAR: Analysis of HER2 inhibitors. 3, 99-106. – reference: Tanada, T. et al., (2000) Development of material design program based on chemometrics. J. Comput. Aided Chem., 1, 35-46. – reference: Matsuoka S., et al., (2000) Development of new 3D-QSAR method by Kohonen network and 3 way PLS analysis. J. Comput. Aided Chem., 1, 22-34. – reference: Lindberg, W., et al., (1983) Partial least-squares method for spectrofluorimetric analysis of mixtures of humic acid and liginsulfonate, Anal. Chem., 55, 643-648. – reference: Pintore, M., et al., (2001) Database mining applied to central nervous system (CNS) activity. Eur. J. Med. Chem., 36, 349-59. – reference: Tamayo, P., et al., (1999) Interpreting patterns of gene expression with self-organizing maps, Proc. Natl. Acad. Sci. USA, 96, 2907-2912. – reference: Kowalski, B., (1975) Chemometrics, J. Chem. Inf. Comput. Sci., 15, 201-203 – reference: Massart, D. L. et al., (1988). "Chemometrics: a text book", Elsevier Amsterdam. – reference: Polanski, J., Walczak, B. (2000) The comparative molecular surface analysis (COMSA): a novel tool for molecular design. Comput. and Chem., 24, 615-25. Hu, S. Y. et al., (2000) Partial least square analysis of lysozyme near-infrared spectra. Appl. Biochem. Biotechnol., 87, 153-63. – reference: PCA (Principal component analysis) – reference: Arakawa, M. et al., (2002) Application of novel molecular alignment method using Hopfield Neural Network to 3D-QSAR. J. Computer Aided Chem., 3, 63-72. – reference: Gregory A. B. et al., (2001) QSARs for 6-azasteroids as inhibitors of human type 1 5?-reductase. J. Chem. Inf. Coput. Sci., 41, 1255-1265. – reference: Hiltunen, H. et al., (2001) Application of self-organizing maps in conformational analysis of lipids. J. Am. Chem. Soc., 123, 810-6. – reference: Marcia, R., et al., (2001) Time course of LPS-induced gene expression in a mouse model of genitourinary inflammation, Physiol. Genomics, 5, 147-160. – reference: Kratochiwil N. A., Huber. W., Muller, F., Kansy, M. Gerber P. R. (2002) Predicting plasma protein binding of drugs: a new approach. Biochem. Pharmacol., 64, 1355-74. – reference: Nishio, H., et al., (2003) Visualization of gene classification based on expression profile using BL-SOM. Proceedings of Workshop on Self-organizing maps, 1-6. – reference: Bleharski J. R., et al., Use of genetic profiling in leprosy to discriminate clinical forms of the disease. Sci., 301, 1527-30. – reference: Schneider, G., (1999) How many potentially secreted proteins are contained in a bacterial genome? Gene, 327, 113-21. – reference: Stanton, D. T. (2003) On the physical interpretation of QSAR models., J. Chem. Inf. Comput. Sci., 43, 1423-33. – reference: Abe, T., et al., (2003) Informatics for unveiling hidden genome signatures. Genome Res., 13, 693-702. – reference: Purohit P. V., Rocke, D. M., (2003) Discriminant models for high-throughput proteomics mass spectrometer data. Proteomics, 3, 1699-703. – reference: Arakawa, M. et al., (2003) Multi-way PLS modeling of structure-activity data by incorporating electrostatic and lipophilic potentials on molecular surface. J. Comput. Aided Chem., 4, 18-26. – reference: Ressom H. et al., (2003) Clustering gene expression data using adaptive double self-organizing map. Neural Networks, 14, 35-46. – reference: Shaw A.D. et al., (2000) Rapid analysis of high-dimensional bioprocesses using multivariate spectroscopies and advanced chemometrics. Adv. Biochem.Eng.Biotechnol., 66, 83-113. – reference: Brown, S. et al., (1992) Chemometrics, Anal. Chem., 64, 22R-49R. – reference: PLS (Partial Least Squares) – reference: Hirai, M.Y. et al, (2004) Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana, Proc. Natl. Acad. Sci. USA, 101,10205-10210. – reference: Shen, Q. et al., (2003) Quantitative structure-activity relationships (QSAR): studies of inhibitors of tyrosine kinase. Eur. J. Pharm. Sci., 20, 63-71. – reference: Pascual-Montano, A. et al., (2002) Quantitative self-organizing maps for clustering electron tomograms. J. Struct. Biol., 138, 114-22. – reference: Wold S. (1991) Chemometrics, why, what and where to next? J. Pharm. Biomed. Anal., 9, 589-96. – reference: Shepard, R. N., (1962) The analysis of proximities: mutldimensional scaling with an unknown distance function. II. Psychometrika, 27, 219-246. – reference: Kirew D.B., et al., (1998) Application of Kohonen Neural Networks in classification of biological active compounds. SAR QSAR Environ. Res., 8, 93-107. – reference: Dutta, R., et al., (2003) Electronic nose based tea quality standardization. Neural Networks, 16, 847-853. – reference: Hasegawa, K. et al., (2001) Nonliniear modeling a structure-activity data by combining genetic algorithms and counter propagation neural networks. J. Comput. Aided Chem., 2, 11-20. – reference: Chemometricsに関するチュートリアルページが以下のサイトで公開されている。このサイトは必見。 – reference: Kihara, M. et al., (2002) Chemical information analysis of coal parameter. J. Comput. aided Chem. 3, 1-7. – reference: Arakawa, M. et al., (2000) Selection of bioactive conformations and alignment rules by 4way PLS analysis: Validation using 3D-structure of protein. J. Comput. Aided Chem. 1, 1-7. – reference: Zheng, G., Huang W. H., Lu, X. H., (2003) Prediction of n-octanol/water partition coefficients for polychlorinated dibenzo-p-dioxins using a general regression neural network. Anal. Bioanal. Chem. 376, 680-5. – reference: Igor V. T. et al., (2001) Volume learning algorithm artificial neural networks for 3D QSAR studies. J. Med. Chem., 44, 2411-2420. |
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| Title | 化学データにおける線形・非線形モデリング |
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