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Data submission for A-0k6f
List of biomarker genes used to predict estrogen receptor activity in MCF-7 cells; list of microarray accession numbers used in the study. This dataset is associated with the following publication: Vanduyn, N., B. Chorley , R. Tice, R. Judson , and C. Corton. Moving Toward Integrating Gene Expression Profiling into High-throughput Testing:A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium. TOXICOLOGICAL SCIENCES. Society of Toxicology, 151(1): 88-103, (2016).
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Dataset for ORD-033374: A Gene Expression Biomarker Identifies Chemical Modulators of the Estrogen Receptor α (ERα) in a MCF-7 Microarray Compendium
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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).
Datasets in Gene Expression Omnibus used in the study ORD-020382: Evaluation of estrogen receptor alpha activation by glyphosate-based herbicide constituents
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GEO accession number of the microarray study. This dataset is associated with the following publication: Mesnage, R., A. Phedonos, M. Biserni, M. Arno, S. Balu, C. Corton, R. Ugarte, and M. Antoniou. Evaluation of estrogen receptor alpha activation by glyphosate-based herbicide constituents. FOOD AND CHEMICAL TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 108: 30-42, (2017).
Data submission for A-d25f
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Includes 1) list of genes in the STAT5b biomarker and 2) list of accession numbers for microarray datasets used in study. This dataset is associated with the following publication: Oshida, K., N. Vasani, D. Waxman, and C. Corton. Disruption of STAT5b-Regulated Sexual Dimorphism of the Liver Transcriptome by Diverse Factors Is a Common Event. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 11(3): NA, (2016).
Supporting data for Suen et al A-0zpd
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Tables, Figures, and Supplemental Materials for doi: 10.1158/1541-7786.MCR-16-0084. This dataset is associated with the following publication: Suen, A., W. Jefferson, C. Wood, E. Padilla-Banks, V. Bae-Jump, and C. Williams. SIX1 Oncoprotein as a Biomarker in a Model of Hormonal Carcinogenesis and in Human Endometrial Cancer.. Molecular Cancer Research. American Association for Cancer Research, Inc., Philadelphia, PA, USA, 14(9): 849-858, (2016).
Datasets for Figures and Tables in SIX1 regulates aberrant endometrial epithelial cell differentiation and cancer trajectory
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Data associated with the figures presented in this study are images, graphics, or tabulated data based on histopathologic analysis performed by a certified study pathologist or image analysis. Data for Tables 1 and 2 provide incidence, labeling scores, and p-values for statistical tests for uterine pathology and IHC expression by treatment group and timepoint or human endometrial tissue, as described in the Main Text file of the manuscript. Manual histopathologic data can be found in excel spreadsheets and are based on presence/absences (yes/no) of a pathologic finding and/or severity score as described in the manuscript (Fig. 1, 2, 3, 4). The image analysis data is based on the quantified area that is designated as “positive” for a particular immunohistochemical stain (Fig. 2, 4, and Suppl. Fig. S2). Supplementary Table 1 provides summary information on antibodies used for immunohistochemistry. Supplementary fig. S1 includes real time RT-PCR data standardized to a housekeeping gene and western blot data. This dataset is associated with the following publication: Suen, A., W. Jefferson, C. Wood, and C. Williams. SIX1 regulates aberrant endometrial epithelial cell differentiation and cancer trajectory. Molecular Cancer Research. American Association for Cancer Research, Inc., Philadelphia, PA, USA, 17(12): 2369-2382, (2019).
CERAPP: Collaborative Estrogen Receptor Activity Prediction Project
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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).
In vitro and in vivo estrogen receptor data sets
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In vitro and in vivo data for the estrogen receptor. The in vivo data is for binding, agonism, and antagonism. The in vivo data is from mouse uterotropic assay data. The following columns are provided in each data set: molecular id, SMILES structure, class (1=active, 0 = inactive), and set (T=training, P=prediction set). This dataset is associated with the following publication: Martin , T. Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering. SAR AND QSAR IN ENVIRONMENTAL RESEARCH. Taylor & Francis, Inc., Philadelphia, PA, USA, 27(1): 17-30, (2016).
In vitro and in vivo estrogen receptor data sets
공공데이터포털
In vitro and in vivo data for the estrogen receptor. The in vivo data is for binding, agonism, and antagonism. The in vivo data is from mouse uterotropic assay data. The following columns are provided in each data set: molecular id, SMILES structure, class (1=active, 0 = inactive), and set (T=training, P=prediction set). This dataset is associated with the following publication: Martin , T. Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering. SAR AND QSAR IN ENVIRONMENTAL RESEARCH. Taylor & Francis, Inc., Philadelphia, PA, USA, 27(1): 17-30, (2016).
Datasets used in ORD-018902: Bisphenol A alternatives can effectively substitute for estradiol
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Gene Expression Omnibus numbers only. This dataset is associated with the following publication: Mesnage, R., A. Phedonos, M. Arno, S. Balu, C. Corton, and M. Antoniou. Transcriptome profiling reveals bisphenol A alternatives activate estrogen receptor alpha in human breast cancer cells. TOXICOLOGICAL SCIENCES. Society of Toxicology, 158(2): 431-443, (2017).
In vitro transcriptomic analyses reveal pathway perturbations, estrogenic activities, and potencies of data-poor BPA alternative chemicals
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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).