ORegAnno: an open access database and curation system for literature-derived promoters, transcription factor binding sites and regulatory variation
Bibliographic record
Abstract
MOTIVATION: Our understanding of gene regulation is currently limited by our ability to collectively synthesize and catalogue transcriptional regulatory elements stored in scientific literature. Over the past decade, this task has become increasingly challenging as the accrual of biologically validated regulatory sequences has accelerated. To meet this challenge, novel community-based approaches to regulatory element annotation are required. SUMMARY: Here, we present the Open Regulatory Annotation (ORegAnno) database as a dynamic collection of literature-curated regulatory regions, transcription factor binding sites and regulatory mutations (polymorphisms and haplotypes). ORegAnno has been designed to manage the submission, indexing and validation of new annotations from users worldwide. Submissions to ORegAnno are immediately cross-referenced to EnsEMBL, dbSNP, Entrez Gene, the NCBI Taxonomy database and PubMed, where appropriate. AVAILABILITY: ORegAnno is available directly through MySQL, Web services, and online at http://www.oreganno.org. All software is licensed under the Lesser GNU Public License (LGPL).
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.025 |
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".