PREDICTION OF SECONDARY STRUCTURE OF RNAs WITH PSEUDOKNOTS USING MATCHED FILTERS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Prediction of ribonucleic acid (RNA) secondary structure is an important task in bioinformatics. The RNA structure is known to influence its biological functionality. RNA secondary structure contains many substructures such as stems, loops and pseudoknots. The substructure pseudoknot occurs in several classes of RNAs, and plays a vital role in many biological processes. Prediction of pseudoknots in RNA is challenging and still an open research problem. Several computational methods based on dynamic programming, genetic algorithms, statistical models, etc., have been proposed with varying success. In this paper, we employ matched filtering approach to determine the RNA secondary structure containing pseudoknots. The central idea is to use a matched filter to identify the longest possible stem patterns in the base-pairing matrix of an RNA. The stem patterns obtained are then used to determine the locations of the other substructures such as loops and pseudoknots present in the RNA. Comparison of the prediction results, for RNA sequences derived from PseudoBase, illustrate the effectiveness and the accuracy of our proposed approach as compared to some of the existing popular RNA secondary structure prediction methods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it