Depth Annotation of RNA Folds for Secondary Structure Motif Search
Bibliographic record
Abstract
The biological activity of RNA depends on the way it folds into secondary structures. Presented here is a framework for exploratory motif searching in the space of RNA secondary structures. A collection of RNA sequences, suspected of having a particular biological activity, is fragmented into overlapping pieces of a uniform size. Each piece is folded and the details of the fold are used to annotate the primary structure. Distances between annotated structures are computed. The distance matrix for the structures is then projected into the Euclidean plane for visualization and detection of clusters. A motif is taken to be a cluster in the two dimensional space. An instance of the framework is implemented for testing on a data set containing examples of the Iron Response Element in the following manner. Folding is performed with the Mfold package. A depth-of-fold that records stems and loops onto the primary sequence is used to annotate the pieces of RNA. Dynamic programming is used to find distances between pieces of annotated primary sequence. An evolutionary algorithm is then used to find a one-to-one mapping of pieces of RNA to points in the plane that has acceptable distortion of the distances found with dynamic programming. This one-to-one mapping is a form of non-linear projection that optimizes for fidelity of projected distances to the distances derived from the Iron Response Element data set.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".