The Transcriptomics Pain Signature Database
Bibliographic record
Abstract
ABSTRACT The availability of convenient tools is critical for the efficient analyses of fast-generated omics-wide-level studies. Here, we describe the creation, characterization, and applications of the Pain Signatures Database (TPSDB), a comprehensive database containing the results of differential gene expression analyses from 338 full transcriptomic datasets for pain-related phenotypes. The database allows searching for a specific gene(s), pathway(s), or SNP(s), or downloading the raw data for hypothesis-free analysis. We took advantage of this unique dataset of multiple pain transcriptomics in several ways. The pathway analyses found the cytokine production regulation and innate immune response the most frequently shared pathways across tissues and conditions. A machine learning-based approach across datasets identified RNA biomarkers for inflammatory and neuropathic pain in rodent dorsal root ganglion (DRG) with high certainty. Finally, functional annotation of pain-related GWAS results demonstrated that differentially expressed genes can be more informative than the general tissue-specific genes from DRG or spinal cord in partitioning heritability analyses.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.023 |
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".