[Decoding the mode of action of the estrogen receptor through functional genomics].
Bibliographic record
Abstract
Estradiol is a potent growth factor of breast cancer cells and inhibition of its activity has been a basis for the treatment of this disease for a long time. Estradiol exerts its action mainly through a nuclear receptor (ERalpha) that recognizes specific sites in the genome and regulates the transcription of neighboring genes. The identification of the repertoire of estrogen responsive genes is considered an essential step for our comprehension of the biological functions of the hormone and of the molecular mechanisms by which ERalpha control gene expression. The technology combining immunoprecipitation of DNA fragments and hybridization to DNA chips currently allows the rapid identification of transcription factor binding sites on a whole-genome level. The recent utilization of this technology has not only led to the identification of numerous ERalpha target genes in breast cancer cells, but has also revealed that the receptor requires the presence of another transcription factor, known as FOXA1, to activate a specific subset of these genes. These studies have thus shown that factors like FOXA1 can be utilized to compartmentalize the action of the hormone, suggesting new opportunities to target more precisely the action of nuclear receptors for the prevention and treatment of hormone-dependent cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".