Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy.
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Emilio Benfenati | Qasim Chaudhry | Giuseppina Gini | Jean Lou Dorne | G. Gini | E. Benfenati | Q. Chaudhry | J. Dorne
[1] P. Boland. Majority Systems and the Condorcet Jury Theorem , 1989 .
[2] Errol Zeiger,et al. Measuring Intra-Assay Agreement for the Ames Salmonella Assay , 1991 .
[3] David H. Wolpert,et al. Stacked generalization , 1992, Neural Networks.
[4] Roberto Todeschini,et al. Hybrid toxicology expert system: architecture and implementation of a multi-domain hybrid expert system for toxicology , 1998 .
[5] C Helma,et al. Data quality in predictive toxicology: reproducibility of rodent carcinogenicity experiments. , 2001, Environmental health perspectives.
[6] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[7] Subhash C. Bagui,et al. Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.
[8] R. Polikar,et al. Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.
[9] Alexandre Varnek,et al. Correlation of blood-brain penetration using structural descriptors. , 2006, Bioorganic & medicinal chemistry.
[10] Paola Gramatica,et al. Statistical external validation and consensus modeling: a QSPR case study for Koc prediction. , 2007, Journal of molecular graphics & modelling.
[11] R. Mannhold,et al. Novel TOPP descriptors in 3D-QSAR analysis of apoptosis inducing 4-aryl-4H-chromenes: comparison versus other 2D- and 3D-descriptors. , 2007, Bioorganic & medicinal chemistry.
[12] Xueguang Shao,et al. A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples. , 2007, Talanta.
[13] 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.
[14] Wout Slob,et al. A retrospective analysis of developmental toxicity studies in rat and rabbit: what is the added value of the rabbit as an additional test species? , 2008, Regulatory toxicology and pharmacology : RTP.
[15] Anton J Hopfinger,et al. Combined 4D-fingerprint and clustering based membrane-interaction QSAR analyses for constructing consensus Caco-2 cell permeation virtual screens. , 2008, Journal of pharmaceutical sciences.
[16] Raymond T. Ng,et al. A Model-Based Ensembling Approach for Developing QSARs , 2009, J. Chem. Inf. Model..
[17] Huanxiang Liu,et al. Global, local and novel consensus quantitative structure-activity relationship studies of 4-(Phenylaminomethylene) isoquinoline-1, 3 (2H, 4H)-diones as potent inhibitors of the cyclin-dependent kinase 4. , 2009, Analytica chimica acta.
[18] Helmut Segner,et al. Variability of in vivo fish acute toxicity data. , 2009, Regulatory toxicology and pharmacology : RTP.
[19] Giuseppina C. Gini,et al. ENSEMBLING REGRESSION MODELS TO IMPROVE THEIR PREDICTIVITY: A CASE STUDY IN QSAR (QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS) WITH COMPUTATIONAL CHEMOMETRICS , 2009, Appl. Artif. Intell..
[20] Philip Judson,et al. Using In Silico Tools in a Weight of Evidence Approach to Aid Toxicological Assessment , 2010, Molecular informatics.
[21] Kunal Roy,et al. QSAR modeling of toxicity of diverse organic chemicals to Daphnia magna using 2D and 3D descriptors. , 2010, Journal of hazardous materials.
[22] Jerzy Leszczynski,et al. New QSPR equations for prediction of aqueous solubility for military compounds. , 2010, Chemosphere.
[23] Francis W. Zwiers,et al. Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties , 2010 .
[24] T. Ferrari,et al. An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts , 2010, Chemistry Central journal.
[25] E. Matthews,et al. Prediction of drug-related cardiac adverse effects in humans--B: use of QSAR programs for early detection of drug-induced cardiac toxicities. , 2010, Regulatory toxicology and pharmacology : RTP.
[26] A. Gupta,et al. Insights through AM1 calculations into the structural requirement of 3,4,6-substituted-2-quinolone analogs towards FMS kinase inhibitory activity. , 2010, European journal of medicinal chemistry.
[27] Emilio Benfenati,et al. Comparison and Possible Use of In Silico Tools for Carcinogenicity Within REACH Legislation , 2011, Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews.
[28] Emilio Benfenati,et al. Quantitative consensus of bioaccumulation models for integrated testing strategies. , 2012, Environment international.
[29] Akos Tarcsay,et al. Comparative evaluation of pK(a) prediction tools on a drug discovery dataset. , 2012, Journal of pharmaceutical and biomedical analysis.
[30] 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.
[31] M. P. Gómez-Carracedo,et al. Screening oil spills by mid-IR spectroscopy and supervised pattern recognition techniques , 2012 .
[32] Marjana Novic,et al. Quantitative structure-activity relationships (QSARs) using the novel marine algal toxicity data of phenols. , 2012, Journal of molecular graphics & modelling.
[33] H. Buist,et al. The OSIRIS Weight of Evidence approach: ITS for skin sensitisation. , 2013, Regulatory toxicology and pharmacology : RTP.
[34] Emilio Benfenati,et al. Using toxicological evidence from QSAR models in practice. , 2013, ALTEX.
[35] Ralph Kühne,et al. The OSIRIS Weight of Evidence approach: ITS mutagenicity and ITS carcinogenicity. , 2013, Regulatory toxicology and pharmacology : RTP.
[36] Emilio Benfenati,et al. Integration of QSAR models for bioconcentration suitable for REACH. , 2013, The Science of the total environment.
[37] Fernanda Borges,et al. Combining QSAR classification models for predictive modeling of human monoamine oxidase inhibitors. , 2013, European journal of medicinal chemistry.
[38] Zhuoyong Zhang,et al. A novel two-step QSAR modeling work flow to predict selectivity and activity of HDAC inhibitors. , 2013, Bioorganic & medicinal chemistry letters.
[39] Emilio Benfenati,et al. Evaluation of QSAR Models for the Prediction of Ames Genotoxicity: A Retrospective Exercise on the Chemical Substances Registered Under the EU REACH Regulation , 2014, Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews.
[40] Qasim Chaudhry,et al. Application of in silico modelling to estimate toxicity of migrating substances from food packaging. , 2014, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.
[41] Janine Ezendam,et al. Evaluating the performance of integrated approaches for hazard identification of skin sensitizing chemicals. , 2014, Regulatory toxicology and pharmacology : RTP.
[42] Paola Gramatica,et al. Metabolic biotransformation half-lives in fish: QSAR modeling and consensus analysis. , 2014, The Science of the total environment.
[43] T W Schultz,et al. A strategy for structuring and reporting a read-across prediction of toxicity. , 2015, Regulatory toxicology and pharmacology : RTP.
[44] Craig Zwickl,et al. An evaluation of in-house and off-the-shelf in silico models: implications on guidance for mutagenicity assessment. , 2015, Regulatory toxicology and pharmacology : RTP.
[45] Andrew Worth,et al. Chemical Safety Assessment Using Read-Across: Assessing the Use of Novel Testing Methods to Strengthen the Evidence Base for Decision Making , 2015, Environmental health perspectives.
[46] Roberto Todeschini,et al. QSAR models for bioconcentration: is the increase in the complexity justified by more accurate predictions? , 2015, Chemosphere.
[47] Robert Rallo,et al. Prioritization of in silico models and molecular descriptors for the assessment of ready biodegradability. , 2015, Environmental research.
[48] Emilio Benfenati,et al. Predicting persistence in the sediment compartment with a new automatic software based on the k-Nearest Neighbor (k-NN) algorithm. , 2016, Chemosphere.
[49] Emilio Benfenati,et al. Integrating in silico models to enhance predictivity for developmental toxicity. , 2016, Toxicology.
[50] Emilio Benfenati,et al. A new integrated in silico strategy for the assessment and prioritization of persistence of chemicals under REACH. , 2016, Environment international.
[51] A Worth,et al. Consensus of classification trees for skin sensitisation hazard prediction. , 2016, Toxicology in vitro : an international journal published in association with BIBRA.
[52] Ruili Huang,et al. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project , 2016, Environmental health perspectives.
[53] Terry W Schultz,et al. Lessons learned from read-across case studies for repeated-dose toxicity. , 2017, Regulatory toxicology and pharmacology : RTP.
[54] M Vracko,et al. Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors. , 2017, Chemosphere.
[55] Patlewicz Grace,et al. Navigating through the minefield of read-across tools: A review of in silico tools for grouping. , 2017, Computational toxicology.
[56] K. Roy,et al. Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models? , 2018 .
[57] Mark T. D. Cronin,et al. Assessing uncertainty in read-across: Questions to evaluate toxicity predictions based on knowledge gained from case studies , 2019, Computational Toxicology.
[58] Marin Georgiev,et al. Category consistency in the OECD QSAR Toolbox: Assessment and reporting tool to justify read-across , 2019, Computational Toxicology.
[59] Andrea-Nicole Richarz,et al. Identification and description of the uncertainty, variability, bias and influence in quantitative structure-activity relationships (QSARs) for toxicity prediction. , 2019, Regulatory toxicology and pharmacology : RTP.