RPmirDIP Predictions of ~6 Million miRNA-Gene Pairs
Bibliographic record
Abstract
The RPmirDIP and RPmirDIP* predictors were used to re-score all available miRNA-mRNA pairs from the mirDIP database to identify novel putative interactions. The ~30 million pairs from the mirDIP database were re-scored using the RPmirDIP(*) method(s). Only those pairs with a Difference of Scores (DoS), defined as RPmirDIP(*)-mirDIP, with a value greater than 0.5 were published as these comprise the set of most likely candidate interactors for subsequent experimental validation. Of this set of ~6 million pairs, the data were divided into subsets based on a cutoff percentile (top 1%, top 5%, top 10%, top 33%, and bottom 66%). These data are presented in two sorted formats. The first is sorted on the Difference of Scores (DoS, RPmirDIP(*)-mirDIP) to identify pairs exhibiting the greatest augmentation of score over mirDIP due to the application of Reciprocal Perspective (RP). The second is sorted on the RPmirDIP(*) score to identify those pairs that RP is most confident in, regardless of the augmentation over mirDIP. Both formats support the exploration of potentially novel miRNA-mRNA interactions warranting subsequent wet laboratory validation. Note: To avoid data duplication, when selecting the top percentile of pairs (say top 10%), you must also select the top percentile files above that level (i.e. top 5% and top 1% files). For convenience, a single file containing all predictions is made available.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.020 |
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".