Identifying mouse genes putatively transcriptionally regulated by the glucocorticoid receptor
Bibliographic record
Abstract
The Glucocorticoid receptor (GR) is one of many steroid hormone receptors. It controls broad physiological gene networks, confers pathological effects in a range of disease states, and offers an excellent target for therapeutic intervention. Therefore, it is necessary to better understand the mechanisms of GR regulation. In particular, we are interested in better understanding the protein-nucleotide interactions (transcription factors interacting with transcription factor binding sites). Upon glucocorticoids-hormone binding, the GR forms a protein-nucleotide interaction with a specific transcription factor binding site known as a glucocorticoid response element (GRE). This research has employed three different but complementary bioinformatics approaches to identify Mouse genes putatively transcriptionally regulated by GR. Firstly, we focus on the problem of searching for putative GREs in the complete Mouse genome using a position weight matrix. This produced a large number of putative GREs. Most of these are likely false positive predictions. Secondly, two different strategies are used to improve the accuracy of our framework: combinatorial analysis of multiple TFs/modules of TFBSs and phylogenetic footprinting (PF). The number of putative GREs can be reduced by 97.9% using the module of TFBSs analysis, 97.7% using the PF analysis, and 99.9% using both module and PF analyses. In each step, a statistical test has been used to measure the significance of the results.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".