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Lauren E Koval

Showing results (1-10 of 12) with videos related to

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Environmental and Molecular Mutagenesis|September 12, 2025
Identifying Gene Predictors of Chemicals Linked With Breast Cancer: A Machine Learning Analysis of MCF7 Cellular Transcriptomic Screening DataLauren E Koval, Richard Judson, Julia E Rager
Journal of Exposure Science & Environmental Epidemiology|June 16, 2022
Environmental mixtures and breast cancer: identifying co-exposure patterns between understudied vs breast cancer-associated chemicals using chemical inventory informaticsLauren E Koval, Kathie L Dionisio, Katie Paul Friedman, et al.
Toxics|May 27, 2022
Chemical Mixtures in Household Environments: In Silico Predictions and In Vitro Testing of Potential Joint Action on PPARγ in Human Liver CellsCeleste K Carberry, Toby Turla, Lauren E Koval, et al.
Metabolites|October 28, 2025
Development of LC-MS/MS Database Based on 250 Potentially Highly Neuroactive Compounds and Their MetabolitesTaylor Teitelbaum, Haoduo Zhao, Lauren E Koval, et al.
Environmental Science & Technology|November 18, 2022
Wildfire Variable Toxicity: Identifying Biomass Smoke Exposure Groupings through Transcriptomic Similarity ScoringLauren E Koval, Celeste K Carberry, Yong Ho Kim, et al.
Journal of Exposure Science & Environmental Epidemiology|November 21, 2025
Environmental factors influencing hormone receptor positive breast cancer incidence: integrating chemical signatures from dust wipes with self-reported sources of exposureLauren E Koval, Yun-Chung Hsiao, Ellie Jiang, et al.
Computational Toxicology (Amsterdam, Netherlands)|May 20, 2021
Predictive modeling of biological responses in the rat liver using <i>in vitro</i> Tox21 bioactivity: Benefits from high-throughput toxicokineticsCaroline Ring, Nisha S Sipes, Jui-Hua Hsieh, et al.
Environmental Research|December 5, 2024
The environmental neuroactive chemicals list of prioritized substances for human biomonitoring and neurotoxicity testing: A database and high-throughput toxicokinetics approachJulia E Rager, Lauren E Koval, Elise Hickman, et al.
Environment International|July 21, 2022
Wildfires and extracellular vesicles: Exosomal MicroRNAs as mediators of cross-tissue cardiopulmonary responses to biomass smokeCeleste K Carberry, Lauren E Koval, Alexis Payton, et al.
Frontiers in Toxicology|July 11, 2022
Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health ResearchKyle Roell, Lauren E Koval, Rebecca Boyles, et al.
Pageof 2

Showing results (1-10 of 12) with videos related to

Sort By:
Pageof 2
Environmental and Molecular Mutagenesis|September 12, 2025
Identifying Gene Predictors of Chemicals Linked With Breast Cancer: A Machine Learning Analysis of MCF7 Cellular Transcriptomic Screening DataLauren E Koval, Richard Judson, Julia E Rager
Journal of Exposure Science & Environmental Epidemiology|June 16, 2022
Environmental mixtures and breast cancer: identifying co-exposure patterns between understudied vs breast cancer-associated chemicals using chemical inventory informaticsLauren E Koval, Kathie L Dionisio, Katie Paul Friedman, et al.
Toxics|May 27, 2022
Chemical Mixtures in Household Environments: In Silico Predictions and In Vitro Testing of Potential Joint Action on PPARγ in Human Liver CellsCeleste K Carberry, Toby Turla, Lauren E Koval, et al.
Metabolites|October 28, 2025
Development of LC-MS/MS Database Based on 250 Potentially Highly Neuroactive Compounds and Their MetabolitesTaylor Teitelbaum, Haoduo Zhao, Lauren E Koval, et al.
Environmental Science & Technology|November 18, 2022
Wildfire Variable Toxicity: Identifying Biomass Smoke Exposure Groupings through Transcriptomic Similarity ScoringLauren E Koval, Celeste K Carberry, Yong Ho Kim, et al.
Journal of Exposure Science & Environmental Epidemiology|November 21, 2025
Environmental factors influencing hormone receptor positive breast cancer incidence: integrating chemical signatures from dust wipes with self-reported sources of exposureLauren E Koval, Yun-Chung Hsiao, Ellie Jiang, et al.
Computational Toxicology (Amsterdam, Netherlands)|May 20, 2021
Predictive modeling of biological responses in the rat liver using <i>in vitro</i> Tox21 bioactivity: Benefits from high-throughput toxicokineticsCaroline Ring, Nisha S Sipes, Jui-Hua Hsieh, et al.
Environmental Research|December 5, 2024
The environmental neuroactive chemicals list of prioritized substances for human biomonitoring and neurotoxicity testing: A database and high-throughput toxicokinetics approachJulia E Rager, Lauren E Koval, Elise Hickman, et al.
Environment International|July 21, 2022
Wildfires and extracellular vesicles: Exosomal MicroRNAs as mediators of cross-tissue cardiopulmonary responses to biomass smokeCeleste K Carberry, Lauren E Koval, Alexis Payton, et al.
Frontiers in Toxicology|July 11, 2022
Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health ResearchKyle Roell, Lauren E Koval, Rebecca Boyles, et al.
Pageof 2