Single-Cell Level Estrogen Receptor Activity Data
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'This dataset contains normalized single-cell level data for 60 reference compounds analyzed with high throughput microscopy and high content analysis-based experiments that were performed using the GFP-ER⍺:PRL-HeLa cell line. The multidimensional imaging data was used to train a classification model to ultimately predict the impact of unknown compounds on the estrogen receptor, either as agonists or antagonists as outlined in the RMarkdown file. All chemical screening data were normalized to E2-treated control samples using the robust z-score calculation based on single-cell data. In experiments that spanned multiple sample plates, the median and MAD values were calculated for each plate, the median MAD value across all plates was determined, and the robust z-score was calculated using the per-plate median value and the overall median MA' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Classification Analysis","Endocrine Disrupting Chemicals","Estrogen Receptor Activity","High-Throughput Microscopy","Machine Learning","Predictive Modeling"]} <|eot_id|>
 Here is the Description of a dataset. Description:'This dataset contains normalized single-cell level data for 60 reference compounds analyzed with high throughput microscopy and high content analysis-based experiments that were performed using the GFP-ER⍺:PRL-HeLa cell line. The multidimensional imaging data was used to train a classification model to ultimately predict the impact of unknown compounds on the estrogen receptor, either as agonists or antagonists as outlined in the RMarkdown file. All chemical screening data were normalized to E2-treated control samples using the robust z-score calculation based on single-cell data. In experiments that spanned multiple sample plates, the median and MAD values were calculated for each plate, the median MAD value across all plates was determined, and the robust z-score was calculated using the per-plate median value and the overall median MA' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
This dataset contains normalized single-cell level data for 60 reference compounds analyzed with high throughput microscopy and high content analysis-based experiments that were performed using the GFP-ER⍺:PRL-HeLa cell line. The multidimensional imaging data was used to train a classification model to ultimately predict the impact of unknown compounds on the estrogen receptor, either as agonists or antagonists as outlined in the RMarkdown file. All chemical screening data were normalized to E2-treated control samples using the robust z-score calculation based on single-cell data. In experiments that spanned multiple sample plates, the median and MAD values were calculated for each plate, the median MAD value across all plates was determined, and the robust z-score was calculated using the per-plate median value and the overall median MA
Classification Analysis; Endocrine Disrupting Chemicals; Estrogen Receptor Activity; High-Throughput Microscopy; Machine Learning; Predictive Modeling