An Approach to Selecting Putative RNA Motifs Using MDL Principle.
Bibliographic record
Abstract
Abstract—The history of molecular biology is punctuated by a series of discoveries demonstrating the surprising breadth of biological roles of ribonucleic acid (RNA). An ensemble of evolutionary related RNA sequences believed to contain signals at sequence and structure level can be exploited to detect motifs common to all or a portion of those sequences. Finding these similar structural features can provide substantial information as to which parts of the sequence are functional. For several decades, free energy minimization has been the most popular method for structure prediction. However, limitations of the free energy models as well as time complexity have prompted us to look for alternative approaches. We therefore, investigate another paradigm, minimum description length (MDL) encoding, for evaluating the significance of consensus motifs. Here, we evaluate motifs generated by Seed using the description length as a selection criteria. MDL scoring method was tested on four data sets of varying complexity. We found that the scoring method produces competing structures in comparison to the ones predicted with lowest free energy. The top rank motifs have high measures of positive predicted value to known motifs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".