RNADPCompare: An algorithm for comparing RNA secondary structures based on image processing techniques
Bibliographic record
Abstract
In structural biology, structural chemistry, and bioinformatics, Ribonucleic Acid (RNA) structure comparison is a fundamental problem. It is because structural comparison can facilitate RNA structure prediction and studies in RNA energy landscapes and conformational switches as well. There are many different tools have been proposed for RNA secondary structure comparison. This paper describes and presents a novel algorithm, RNADPCompare, for computing similarity measure of RNA secondary structures. The main idea for this algorithm is to represent the RNA secondary structure as a dot plot, and then process the dot plot as an image. The algorithm will utilize image processing techniques and heuristic understanding of the image properties to compute similarity measure of RNA secondary structures. Since many evolutionary and machine learning algorithms for RNA secondary structure design and prediction rely on good metric for examining structural similarities, therefore this novel metric will make significant contribution to the advances to these algorithms. An evaluation of the algorithm in terms of correlation to the native structure is made. The results from the six sequences of RNA from a variety of sequence lengths and organisms were tested. When comparing with Sfold, the prediction accuracy of using RNADPCompare to compute the difference matrix seems to be very promising. These results demonstrated that RNADPCompare is highly competitive in terms of the processing speed and accuracy when compare to other methods. This supports the use of this algorithm on other research in RNA secondary structure design and prediction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".