about
Low-Dose Mixture Hypothesis of Carcinogenesis Workshop: Scientific Underpinnings and Research RecommendationsA computational model predicting disruption of blood vessel developmentIntegrated decision strategies for skin sensitization hazardPredicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compoundsAssessing the carcinogenic potential of low-dose exposures to chemical mixtures in the environment: the challenge aheadAssessing the carcinogenic potential of low-dose exposures to chemical mixtures in the environment: focus on the cancer hallmark of tumor angiogenesisEvaluation of 309 environmental chemicals using a mouse embryonic stem cell adherent cell differentiation and cytotoxicity assayPhenotypic screening of the ToxCast chemical library to classify toxic and therapeutic mechanismsSupporting read-across using biological dataMultivariate Models for Prediction of Human Skin Sensitization HazardDevelopment and Validation of a Computational Model for Androgen Receptor ActivityEnvironmental impact on vascular development predicted by high-throughput screening.Predictive models of prenatal developmental toxicity from ToxCast high-throughput screening data.Identifying developmental toxicity pathways for a subset of ToxCast chemicals using human embryonic stem cells and metabolomics.Zebrafish developmental screening of the ToxCast™ Phase I chemical library.Predictive models and computational toxicology.A C. elegans screening platform for the rapid assessment of chemical disruption of germline function.Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitizationIdentifying environmental chemicals as agonists of the androgen receptor by using a quantitative high-throughput screening platform.QSAR models of human data can enrich or replace LLNA testing for human skin sensitization.A Curated Database of Rodent Uterotrophic Bioactivity.Integration of Life-Stage Physiologically Based Pharmacokinetic Models with Adverse Outcome Pathways and Environmental Exposure Models to Screen for Environmental Hazards.Disruption of embryonic vascular development in predictive toxicology.Prediction of skin sensitization potency using machine learning approaches.Identification of vascular disruptor compounds by analysis in zebrafish embryos and mouse embryonic endothelial cells.An Integrated Chemical Environment to Support 21st-Century Toxicology.Embryonic vascular disruption adverse outcomes: Linking high throughput signaling signatures with functional consequences.Pred-Skin: A Fast and Reliable Web Application to Assess Skin Sensitization Effect of Chemicals.Embryonic vascular disruption adverse outcomes: Linking high-throughput signaling signatures with functional consequences.A design thinking approach to primary ovarian insufficiency.Screening for angiogenic inhibitors in zebrafish to evaluate a predictive model for developmental vascular toxicity.In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning.Update on EPA's ToxCast program: providing high throughput decision support tools for chemical risk management.Editor's Highlight: Analysis of the Effects of Cell Stress and Cytotoxicity on In Vitro Assay Activity Across a Diverse Chemical and Assay Space.Toward Good Read-Across Practice (GRAP) guidanceScreening Chemicals for Estrogen Receptor Bioactivity Using a Computational Model.Open source software implementation of an integrated testing strategy for skin sensitization potency based on a Bayesian network.Activity profiles of 309 ToxCast™ chemicals evaluated across 292 biochemical targets.Integrated Testing Strategies (ITS) for safety assessment.In vitro perturbations of targets in cancer hallmark processes predict rodent chemical carcinogenesis.
P50
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P50
description
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Nicole C. Kleinstreuer
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Nicole C. Kleinstreuer
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Nicole C. Kleinstreuer
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Nicole C. Kleinstreuer
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Nicole C. Kleinstreuer
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Nicole C. Kleinstreuer
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Nicole C. Kleinstreuer
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Nicole C. Kleinstreuer
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Nicole Kleinstreuer
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Nicole C. Kleinstreuer
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Nicole C. Kleinstreuer
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Nicole C. Kleinstreuer
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Nicole C. Kleinstreuer
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Nicole C. Kleinstreuer
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P1053
F-7203-2019
P106
P1153
38961472700
P21
P2798
P31
P496
0000-0002-7914-3682