about
ToxCast Chemical Landscape: Paving the Road to 21st Century ToxicologyCERAPP: Collaborative Estrogen Receptor Activity Prediction ProjectAn automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modellingDocking-based classification models for exploratory toxicology studies on high-quality estrogenic experimental data.Quantitative structure-activity relationship models for ready biodegradability of chemicals.Integrated Model of Chemical Perturbations of a Biological Pathway Using 18 In Vitro High-Throughput Screening Assays for the Estrogen Receptor.In Silico Study of In Vitro GPCR Assays by QSAR Modeling.Prediction of Estrogenic Bioactivity of Environmental Chemical Metabolites.In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning.Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure.The CompTox Chemistry Dashboard: a community data resource for environmental chemistry.Assessing bioaccumulation of polybrominated diphenyl ethers for aquatic species by QSAR modeling.Suspect screening and non-targeted analysis of drinking water using point-of-use filters.Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals.High-throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling.Evaluating opportunities for advancing the use of alternative methods in risk assessment through the development of fit-for-purpose in vitro assays.Predicting in vivo effect levels for repeat-dose systemic toxicity using chemical, biological, kinetic and study covariates.Comparison of different approaches to define the applicability domain of QSAR models.OPERA models for predicting physicochemical properties and environmental fate endpoints.Rapid experimental measurements of physicochemical properties to inform models and testingA systematic evaluation of analogs and automated read-across prediction of estrogenicity: A case study using hindered phenolsAddressing systematic inconsistencies between in vitro and in vivo transcriptomic mode of action signaturesHigh-Throughput Screening to Predict Chemical-Assay InterferenceA Workflow for Identifying Metabolically Active Chemicals to Complement in vitro Toxicity Screening
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description
hulumtues
@sq
researcher
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wetenschapper
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հետազոտող
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name
Kamel Mansouri
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Kamel Mansouri
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Kamel Mansouri
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Kamel Mansouri
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Kamel Mansouri
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type
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Kamel Mansouri
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Kamel Mansouri
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Kamel Mansouri
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Kamel Mansouri
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Kamel Mansouri
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prefLabel
Kamel Mansouri
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Kamel Mansouri
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Kamel Mansouri
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Kamel Mansouri
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Kamel Mansouri
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0000-0002-6426-8036