Chemogenomics for steroid hormone receptors (NR3)
Bibliographic record
Abstract
The nine human NR3 nuclear receptors translate steroid hormone signals in transcriptomic responses and operate multiple highly important processes ranging from development over reproductive tissue function to inflammatory and metabolic homeostasis. Although several NR3 ligands such as glucocorticoids are invaluable drugs, this family is only partially explored, for example, in autoimmune diseases and neurodegeneration, but may hold therapeutic potential in new areas. Here we report a chemogenomics (CG) library to reveal elusive effects of NR3 receptor modulation in phenotypic settings. 34 highly annotated and chemically diverse ligands covering all NR3 receptors were selected considering complementary modes of action and activity, selectivity and lack of toxicity. Endoplasmic reticulum stress resolving effects of N3 CG subsets in proof-of-concept application validate suitability of the set to connect phenotypic outcomes with targets and to explore NR3 receptors from a translational perspective.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".