Tabu Search for the RNA Partial Degradation Problem
Bibliographic record
Abstract
Abstract In recent years, a growing interest has been observed in research on RNA (ribonucleic acid), primarily due to the discovery of the role of RNA molecules in biological systems. They not only serve as templates in protein synthesis or as adapters in the translation process, but also influence and are involved in the regulation of gene expression. The RNA degradation process is now heavily studied as a potential source of such riboregulators. In this paper, we consider the so-called RNA partial degradation problem (RNA PDP). By solving this combinatorial problem, one can reconstruct a given RNA molecule, having as input the results of the biochemical analysis of its degradation, which possibly contain errors (false negatives or false positives). From the computational point of view the RNA PDP is strongly NP-hard. Hence, there is a need for developing algorithms that construct good suboptimal solutions. We propose a heuristic approach, in which two tabu search algorithms cooperate, in order to reconstruct an RNA molecule. Computational tests clearly demonstrate that the proposed approach fits well the biological problem and allows to achieve near-optimal results. The algorithm is freely available at http://www.cs.put.poznan.pl/arybarczyk/tabusearch.php.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".