Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches.
暂无分享,去创建一个
[1] Malcolm C. Pike,et al. The TD50: a proposed general convention for the numerical description of the carcinogenic potency of chemicals in chronic-exposure animal experiments. , 1984 .
[2] R. Benigni. Structure-activity relationship studies of chemical mutagens and carcinogens: mechanistic investigations and prediction approaches. , 2005, Chemical reviews.
[3] David B. Dunson,et al. Bayesian Data Analysis , 2010 .
[4] Andrew P Worth,et al. Comparison of the applicability domain of a quantitative structure‐activity relationship for estrogenicity with a large chemical inventory , 2006, Environmental toxicology and chemistry.
[5] R. Saracci,et al. Describing the validity of carcinogen screening tests. , 1979, British Journal of Cancer.
[6] C W Yap,et al. Classification of a diverse set of Tetrahymena pyriformis toxicity chemical compounds from molecular descriptors by statistical learning methods. , 2006, Chemical research in toxicology.
[7] B. LeBaron,et al. A test for independence based on the correlation dimension , 1996 .
[8] Nikolaus Stiefl,et al. Structural resemblances and comparisons of the relative pharmacological properties of imatinib and nilotinib. , 2010, Bioorganic & medicinal chemistry.
[9] Ulrike Bernauer,et al. The use of in vitro data in risk assessment. , 2005, Basic & clinical pharmacology & toxicology.
[10] Emilio Benfenati,et al. The Expanding Role of Predictive Toxicology: An Update on the (Q)SAR Models for Mutagens and Carcinogens , 2007, Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews.
[11] G H Loew,et al. Computer-assisted mechanistic structure-activity studies: application to diverse classes of chemical carcinogens. , 1985, Environmental health perspectives.
[12] Marjan Vracko,et al. A Study of Structure-Carcinogenic Potency Relationship with Artificial Neural Networks. The Using of Descriptors Related to Geometrical and Electronic Structures , 1997, J. Chem. Inf. Comput. Sci..
[13] Emmanuel Anoruo,et al. Testing for Linear and Nonlinear Causality between Crude Oil Price Changes and Stock Market Returns , 2012 .
[14] K. P. Singh,et al. Support vector machines in water quality management. , 2011, Analytica chimica acta.
[15] R. S. Zhang,et al. Quantitative structure-toxicity relationships (QSTRs): A comparative study of various non linear methods. General regression neural network, radial basis function neural network and support vector machine in predicting toxicity of nitro- and cyano- aromatics to Tetrahymena pyriformis , 2006, SAR and QSAR in environmental research.
[16] Nikita Basant,et al. Modeling the performance of "up-flow anaerobic sludge blanket" reactor based wastewater treatment plant using linear and nonlinear approaches--a case study. , 2010, Analytica chimica acta.
[17] Hojjat Adeli,et al. A probabilistic neural network for earthquake magnitude prediction , 2009, Neural Networks.
[18] Didier Villemin,et al. Predicting Carcinogenicity of Polycyclic Aromatic Hydrocarbons from Back-Propagation Neural Network , 1994, Journal of chemical information and computer sciences.
[19] Maykel Pérez González,et al. Quantitative structure activity relationship for the computational prediction of nitrocompounds carcinogenicity. , 2006, Toxicology.
[20] X. Y. Zhang,et al. Application of support vector machine (SVM) for prediction toxic activity of different data sets. , 2006, Toxicology.
[21] T. I. Netzeva,et al. Prediction of estrogenicity: validation of a classification model , 2006, SAR and QSAR in environmental research.
[22] K. Varmuza,et al. Spectral similarity versus structural similarity: infrared spectroscopy , 2003 .
[23] Yin-tak Woo,et al. OncoLogic: A Mechanism-Based Expert System for Predicting the Carcinogenic Potential of Chemicals , 2005 .
[24] John M. Barnard,et al. Chemical Similarity Searching , 1998, J. Chem. Inf. Comput. Sci..
[25] Mati Karelson,et al. Quantitative Structure–Activity Relationship (QSAR) Modeling of EC50 of Aquatic Toxicities for Daphnia magna , 2009, Journal of toxicology and environmental health. Part A.
[26] Premanjali Rai,et al. Predicting adsorptive removal of chlorophenol from aqueous solution using artificial intelligence based modeling approaches , 2013, Environmental Science and Pollution Research.
[27] Emilio Benfenati,et al. Some results for the prediction of carcinogenicity using hybrid systems , 1999 .
[28] R Benigni,et al. Prediction of rodent carcinogenicity of aromatic amines: a quantitative structure-activity relationships model. , 2001, Carcinogenesis.
[29] Roberto Todeschini,et al. Structure/Response Correlations and Similarity/Diversity Analysis by GETAWAY Descriptors, 1. Theory of the Novel 3D Molecular Descriptors , 2002, J. Chem. Inf. Comput. Sci..
[30] Ivan Rusyn,et al. The Use of Cell Viability Assay Data Improves the Prediction Accuracy of Conventional Quantitative Structure Activity Relationship Models of Animal Carcinogenicity , 2007 .
[31] Changwen Du,et al. Prediction of nitrate release from polymer-coated fertilizers using an artificial neural network model , 2008 .
[32] Priyanka Ojha,et al. Partial least squares and artificial neural networks modeling for predicting chlorophenol removal from aqueous solution , 2009 .
[33] Kunal Roy,et al. Development and validation of a robust QSAR model for prediction of carcinogenicity of drugs. , 2011, Indian journal of biochemistry & biophysics.
[34] L. Gold,et al. Supplement to the Carcinogenic Potency Database (CPDB): results of animal bioassays published in the general literature in 1993 to 1994 and by the National Toxicology Program in 1995 to 1996. , 1999, Environmental health perspectives.
[35] Premanjali Rai,et al. Modeling and optimization of reductive degradation of chloramphenicol in aqueous solution by zero-valent bimetallic nanoparticles , 2012, Environmental Science and Pollution Research.
[36] Ann M Richard,et al. A novel approach: chemical relational databases, and the role of the ISSCAN database on assessing chemical carcinogenicity. , 2008, Annali dell'Istituto superiore di sanita.
[37] J. Contrera,et al. A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES software. , 1998, Regulatory toxicology and pharmacology : RTP.
[38] Raghuraman Venkatapathy,et al. Development of quantitative structure-activity relationship (QSAR) models to predict the carcinogenic potency of chemicals I. Alternative toxicity measures as an estimator of carcinogenic potency. , 2009, Toxicology and applied pharmacology.
[39] Tom Fawcett,et al. An introduction to ROC analysis , 2006, Pattern Recognit. Lett..
[40] C. Cooper,et al. Chemical Carcinogenesis and Mutagenesis I , 1990, Handbook of Experimental Pharmacology.
[41] Y. Wang,et al. Using support vector regression coupled with the genetic algorithm for predicting acute toxicity to the fathead minnow , 2010, SAR and QSAR in environmental research.
[42] Luis G Valerio,et al. Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling. , 2007, Toxicology and applied pharmacology.
[43] Scott D. Kahn,et al. Current Status of Methods for Defining the Applicability Domain of (Quantitative) Structure-Activity Relationships , 2005, Alternatives to laboratory animals : ATLA.
[44] Kunal Roy,et al. First report on development of quantitative interspecies structure-carcinogenicity relationship models and exploring discriminatory features for rodent carcinogenicity of diverse organic chemicals using OECD guidelines. , 2012, Chemosphere.
[45] A. Giri. Genetic toxicology of vinyl chloride--a review. , 1995, Mutation research.
[46] R Benigni,et al. Quantitative structure-activity relationships of mutagenic and carcinogenic aromatic amines. , 2000, Chemical reviews.
[47] Desire L. Massart,et al. Local modelling with radial basis function networks , 2000 .
[48] Ralph Kühne,et al. Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses , 2010, Molecular Diversity.
[49] Tomasz Arodz,et al. Computational methods in developing quantitative structure-activity relationships (QSAR): a review. , 2006, Combinatorial chemistry & high throughput screening.
[50] R Posthumus,et al. Validity and validation of expert (Q)SAR systems. , 2005, SAR and QSAR in environmental research.
[51] Shikha Gupta,et al. Linear and nonlinear modeling approaches for urban air quality prediction. , 2012, The Science of the total environment.
[52] Yue Yu,et al. In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods. , 2011, Chemosphere.
[53] A M Richard,et al. A CASE-SAR analysis of polycyclic aromatic hydrocarbon carcinogenicity. , 1990, Mutation research.
[54] Feng Luan,et al. Classification of the carcinogenicity of N-nitroso compounds based on support vector machines and linear discriminant analysis. , 2005, Chemical research in toxicology.
[55] A. Balaban. Highly discriminating distance-based topological index , 1982 .
[56] Maykel Pérez González,et al. A topological substructural approach applied to the computational prediction of rodent carcinogenicity. , 2005, Bioorganic & medicinal chemistry.
[57] Hao Zhu,et al. ESP: A Method To Predict Toxicity and Pharmacological Properties of Chemicals Using Multiple MCASE Databases , 2004, J. Chem. Inf. Model..
[58] Vladimir V Poroikov,et al. Computer-aided rodent carcinogenicity prediction. , 2005, Mutation research.
[59] J. Contrera,et al. Predicting the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices. , 2003, Regulatory toxicology and pharmacology : RTP.
[60] Naomi L Kruhlak,et al. Comparison of MC4PC and MDL-QSAR rodent carcinogenicity predictions and the enhancement of predictive performance by combining QSAR models. , 2007, Regulatory toxicology and pharmacology : RTP.
[61] L Zhang,et al. The structure-activity relationship of skin carcinogenicity of aromatic hydrocarbons and heterocycles. , 1992, Chemico-biological interactions.
[62] Shikha Gupta,et al. Artificial intelligence based modeling for predicting the disinfection by-products in water , 2012 .
[63] Giuseppina C. Gini,et al. Predictive Carcinogenicity: A Model for Aromatic Compounds, with Nitrogen-Containing Substituents, Based on Molecular Descriptors Using an Artificial Neural Network , 1999, J. Chem. Inf. Comput. Sci..
[64] Division on Earth. Risk Assessment in the Federal Government: Managing the Process , 1983 .
[65] Gergana Dimitrova,et al. A Stepwise Approach for Defining the Applicability Domain of SAR and QSAR Models , 2005, J. Chem. Inf. Model..
[66] Sholom M. Weiss,et al. Computer Systems That Learn , 1990 .
[67] Anthony T. C. Goh,et al. Probabilistic neural network for evaluating seismic liquefaction potential , 2002 .
[68] Emilio Benfenati,et al. New public QSAR model for carcinogenicity , 2010, Chemistry Central journal.
[69] N X Tan,et al. Prediction of chemical carcinogenicity by machine learning approaches , 2009, SAR and QSAR in environmental research.
[70] Romualdo Benigni,et al. Designing safer drugs: (Q)SAR-based identification of mutagens and carcinogens. , 2003, Current topics in medicinal chemistry.