Does GATA3 act in tissue-specific pathways? A meta-analysis-based approach
Bibliographic record
Abstract
The GATA3 transcription factor is expressed in many tissues such as the immune system, kidney, brain, endometrium, and mammary epithelial cells. As such it must co-ordinate a diverse transcriptional program to achieve specific outcomes in different tissues. One of the most interesting questions raised is whether GATA3 will be involved in the same pathways in every tissue or will be involved in distinct regulatory networks within different tissue types? While previous studies may imply the latter, with some known targets of GATA3 perhaps being specific to cell-type or tissue-type, the question has not been systematically addressed until now. With the advent of techniques such as co-expression meta-analysis a better understanding of the pathway partners of GATA3 can be obtained and specifically the partners within different tissue types can be found, yielding leads for future studies. Here, a recent technique of meta-analysis from the Oncomine database has been employed to probe this very question. Data obtained implies that GATA3 is involved in distinct pathways in different tissue types.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.026 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".