Evaluation of RNA Secondary Structure Motifs using Regression Analysis
Bibliographic record
Abstract
Recent experimental evidences have shown that ribonucleic acid (RNA) plays a greater role in the cell than previously thought. An ensemble of RNA sequences believed to contain signals at the structure level can be exploited to detect functional motifs common to all or a portion of those sequences. We present here a general framework for analyzing multiple RNA secondary structures. A family of related RNA structures may be analyzed using statistical regression methods. In this work, we extend our previously developed algorithm, seed, that allows to explore exhaustively the search space of RNA sequence and structure motifs. We introduce here several objective functions based on thermodynamic free energy and information content to discriminate native folds from the rest. We assume that the variation across the various scores can be represented by a statistical model. Regression analysis permits to assign separate weight for each score, allowing one to emphasize or compensate the variance that differs across the different scores. A statistical model can be formulated using techniques from regression analysis to obtain a template or scoring model that is able to identify putative functional regions in RNA sequences. We show that thermodynamic based regression models are effective to associate the variation of scores obtained from different functions. The models can generally identify motifs with high measures of specificity and positive predicted value to known motifs. A good scoring method will allow to eliminate invalid motifs thereby reducing the size of the hypothesis space
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".