The Pain Genes Database : An interactive web browser of pain-related transgenic knockout studies
Bibliographic record
Abstract
The transgenic knockout mouse is one of the most important tools of modern biology, and commonly employed by pain researchers to examine the function of genes of interest. Over 400 papers, at a current rate of >60 papers per year, have been published to date describing a statistically significant behavioral pain "phenotype" resulting from the null mutation of a single gene. The standard literature review format is incapable of providing a sufficiently broad and up-to-date overview of the field. We have therefore constructed the Pain Genes Database, an interactive, web-based data browser designed to allow easy access to and analysis of the published pain-related phenotypes of mutant mice (over 200 different mutants at the date of submission). Manuscripts describing results of pain-relevant knockout studies were identified via Medline search. Manuscripts were included in the database if they described the testing of a spontaneous or genetically engineered mutant mouse with null expression of a single gene on a behavioral assay of acute or tonic nociception, injury- or stimulus-induced hypersensitivity (i.e., allodynia or hyperalgesia), or drug- or stress-induced inhibition of nociception (i.e., analgesia), and reported at least one statistically significant difference between the mutant mice and their simultaneously tested wildtype controls. The database features two levels of exploration, one allowing the identification of genes by name, acronym, genomic position or "summary" phenotype, and the other allowing in-depth browsing, paper-by-paper, of specific phenotypes and test parameters. Links to genetic databases and Medline abstracts are provided for each gene and paper. It is our intention to update the database continually based on weekly Medline searches. This database should provide pain researchers with a useful and easy-to-use tool for the generation of novel hypotheses regarding the roles of genes and their protein products in pain processing and modulation. It can be accessed at http://paingeneticslab.ca/4105/06_02_pain_genetics_database.asp (or by visiting paingeneticslab.ca and clicking on the "Pain Genes Db" link under "Resources").
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.081 | 0.048 |
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".