RNA-seq dataset of the estrogen-dependent regulation of the transcriptome in mouse mammary gland organoids
Bibliographic record
Abstract
The mammary gland development in utero and during life is strongly regulated by hormones. To study the genes regulated specifically by the estrogen signalling pathway in the epithelial compartment, we treated mouse mammary epithelial organoids with estradiol, the most potent endogenous estrogen. At maturity, after 11 days of treatment, organoids were collected, and RNA was purified for next-generation sequencing. The bulk mRNA-seq data obtained were verified for raw quality, and reads were pseudo-aligned on the murine reference transcriptome (Gencode vM25). Differentially expressed genes were identified using DESeq2 to gain a better understanding of the impact of estrogens on the mammary epithelial cell transcriptome ex vivo . These data can be reanalyzed and combined with recent single-cell RNA-seq data to study the estrogen-dependent transcriptome at the cellular level and better understand the functional impact on the mammary gland in physiopathological conditions, such as during lactation, following endocrine-disrupting chemical exposure, or through the course of carcinogenesis.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".