Tabu Search for the RNA Partial Degradation Problem
Why this work is in the frame
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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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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