The Case for Standardising Gene Nomenclature Across Vertebrates
Bibliographic record
Abstract
Standardized gene nomenclature supports unambiguous communication and identification of the scientific literature associated with genes. To support the increasing number of annotated genomes that are now available for comparative studies, gene nomenclature authorities coordinate the assignment of approved gene names that can be readily propagated across species without losing their sense of meaning. Theofanopoulou et al (Theofanopoulou et al. 2021) propose nomenclature changes to the genes encoding oxytocin and arginine vasopressin and their receptors which would hinder comparative studies and literature identification. Instead, we propose minor updates to the current approved nomenclature of these vertebrate genes to better reflect their evolutionary history, without confusing the literature that already exists around these well-studied genes. We encourage authors to work with nomenclature committees to ensure any novel gene names fit current guidelines so that their publications can be readily indexed and made accessible. Moreover, we call on journal editors and reviewers to help support communication and indexing of gene-related publications by ensuring that standardized gene nomenclature is routinely used.
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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 | 0.253 | 0.316 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.022 | 0.041 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.011 | 0.028 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".