This is an original dataset generated at the U.S. EPA. Data was analyzed with the ToxCast Pipeline and deposited for release in invitroDB accessible via the U.S. EPA CompTox Chemicals Dashboard. Dataset is a zip file containing two Excel spreadsheets titled AIME-ERTA_384_Tables_All_Submission_v2 and README_AIME-ERTA_384_Manuscript_Sup_Data_v2. This dataset is associated with the following publication: Hopperstad, K., D. DeGroot, T. Zurlinden, C. Brinkman, R. Thomas, and C. Deisenroth. Chemical Screening in an Estrogen Receptor Transactivation Assay with Metabolic Competence. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 187(1): 112-126, (2022).

Dataset consists of high throughput in vitro bioactivity data and exposure predictions from the U.S. EPA’s Toxicity and Exposure Forecaster (ToxCast and ExpoCast) project. This dataset is associated with the following publication: Wegner, S., C. Pinto, C. Ring, and J. Wambaugh. High-throughput screening tools facilitate calculation of a combined exposure-bioactivity index for chemicals with endocrine activity. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 137: 105470, (2020).

Data from a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) demonstrating using predictive computational models on high-throughput screening data to screen thousands of chemicals against the estrogen receptor. This dataset is associated with the following publication: Mansouri , K., A. Abdelaziz, A. Rybacka, A. Roncaglioni, A. Tropsha, A. Varnek, A. Zakharov, A. Worth, A. Richard , C. Grulke , D. Trisciuzzi, D. Fourches, D. Horvath, E. Benfenati , E. Muratov, E.B. Wedebye, F. Grisoni, G.F. Mangiatordi, G.M. Incisivo, H. Hong, H.W. Ng, I.V. Tetko, I. Balabin, J. Kancherla , J. Shen, J. Burton, M. Nicklaus, M. Cassotti, N.G. Nikolov, O. Nicolotti, P.L. Andersson, Q. Zang, R. Politi, R.D. Beger , R. Todeschini, R. Huang, S. Farag, S.A. Rosenberg, S. Slavov, X. Hu, and R. Judson. (Environmental Health Perspectives) CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 1-49, (2016).

Data from a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) demonstrating using predictive computational models on high-throughput screening data to screen thousands of chemicals against the estrogen receptor. This dataset is associated with the following publication: Mansouri , K., A. Abdelaziz, A. Rybacka, A. Roncaglioni, A. Tropsha, A. Varnek, A. Zakharov, A. Worth, A. Richard , C. Grulke , D. Trisciuzzi, D. Fourches, D. Horvath, E. Benfenati , E. Muratov, E.B. Wedebye, F. Grisoni, G.F. Mangiatordi, G.M. Incisivo, H. Hong, H.W. Ng, I.V. Tetko, I. Balabin, J. Kancherla , J. Shen, J. Burton, M. Nicklaus, M. Cassotti, N.G. Nikolov, O. Nicolotti, P.L. Andersson, Q. Zang, R. Politi, R.D. Beger , R. Todeschini, R. Huang, S. Farag, S.A. Rosenberg, S. Slavov, X. Hu, and R. Judson. (Environmental Health Perspectives) CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 1-49, (2016).

Screening certain environmental chemicals for their ability to interact with endocrine targets, including the androgen receptor (AR), is an important global concern. We previously developed a model using a battery of eleven in vitro AR assays to predict in vivo AR activity. Here we describe a revised mathematical modelling approach that also incorporates data from newly available assays and demonstrate that subsets of assays can provide close to the same level of predictivity. These subset models are evaluated against the full model using 1820 chemicals, as well as in vitro and in vivo reference chemicals from the literature. This dataset is associated with the following publication: Judson, R., K. Houck, K. Friedman, J. Brown, P. Browne, P. Johnston, D. Close, K. Mansouri, and N. Kleinstreuer. Selecting a Minimal set of Androgen Receptor Assays for Screening Chemicals. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 117(November 2020): 104764, (2020).

Screening certain environmental chemicals for their ability to interact with endocrine targets, including the androgen receptor (AR), is an important global concern. We previously developed a model using a battery of eleven in vitro AR assays to predict in vivo AR activity. Here we describe a revised mathematical modelling approach that also incorporates data from newly available assays and demonstrate that subsets of assays can provide close to the same level of predictivity. These subset models are evaluated against the full model using 1820 chemicals, as well as in vitro and in vivo reference chemicals from the literature. This dataset is associated with the following publication: Judson, R., K. Houck, K. Friedman, J. Brown, P. Browne, P. Johnston, D. Close, K. Mansouri, and N. Kleinstreuer. Selecting a Minimal set of Androgen Receptor Assays for Screening Chemicals. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 117(November 2020): 104764, (2020).

GEOSite dataset for article 'In vitro transcriptomic analyses reveal pathway perturbations, estrogenic activities, and potencies of data-poor BPA alternative chemicals '. This dataset is associated with the following publication: Matteo, G., K. Leingartner, A. Rowan-Carroll, M. Meier, A. Williams, M. Beal, M. Gagne, R. Farmahin, S. Wickramasuriya, A.J. Reardon, T. Barton-Maclaren, J. Corton, C. Yauk, and E. Atlas. In vitro transcriptomic analyses reveal pathway perturbations, estrogenic activities, and potencies of data-poor BPA alternative chemicals. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 191(2): 266-275, (2023).

GEOSite dataset for article 'In vitro transcriptomic analyses reveal pathway perturbations, estrogenic activities, and potencies of data-poor BPA alternative chemicals '. This dataset is associated with the following publication: Matteo, G., K. Leingartner, A. Rowan-Carroll, M. Meier, A. Williams, M. Beal, M. Gagne, R. Farmahin, S. Wickramasuriya, A.J. Reardon, T. Barton-Maclaren, J. Corton, C. Yauk, and E. Atlas. In vitro transcriptomic analyses reveal pathway perturbations, estrogenic activities, and potencies of data-poor BPA alternative chemicals. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 191(2): 266-275, (2023).

Microarray experiments examined in the study. This dataset is associated with the following publication: Rooney, J., N. Ryan, J. Liu, R. Houtman, R. van Beuningen, J. Hsieh, G. Chang, S. Chen, and J. Corton. A Gene Expression Biomarker Identifies Chemical Modulators of Estrogen Receptor α in an MCF-7 Microarray Compendium. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 34(2): 313-329, (2021).

Microarray experiments examined in the study. This dataset is associated with the following publication: Rooney, J., N. Ryan, J. Liu, R. Houtman, R. van Beuningen, J. Hsieh, G. Chang, S. Chen, and J. Corton. A Gene Expression Biomarker Identifies Chemical Modulators of Estrogen Receptor α in an MCF-7 Microarray Compendium. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 34(2): 313-329, (2021).