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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".