The 25th Volume: Role of the GATA Family of Transcription Factors in Andrology
Bibliographic record
Abstract
Understanding how genes get turned on or off is central to the study of biological processes in both health and disease. Several regulatory mechanisms, which can be positive or negative, have been implicated in the control of tissue- and cell-specific gene expression. These include modulation of chromatin structure, DNA methylation, and the regulation of transcription and translation. Transcription factors are nuclear regulatory proteins that bind specific DNA sequences in the 5 � regulatory or promoter regions of target genes. They are involved in both basal and tissue-specific gene expression. There are several classes of transcription factors that have been defined based on similarities in the structure of the respective DNA-binding domains. They include zinc finger, helixloop-helix, leucine zipper, and homeobox transcription factors. The GATA family of zinc finger transcription factors is named from the consensus nucleotide sequence (A/ TGATAA/G) that these factors bind in the promoter regions of target genes. They were originally identified as crucial regulators of heart development and the differentiation of blood and immune cells. GATA expression, however, is not limited to these two systems. Indeed, reproductive tissues such as the testis and ovary are also prominent sites of GATA expression. As few as 5 years ago, the role of GATA factors in reproductive function was uncharted territory. With the recent contributions to the field, the scientific community has come a long way in filling this void. GATA factors have now been impli-
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.077 | 0.049 |
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".