Restricted DCJ Model: Rearrangement Problems with Chromosome Reincorporation
Bibliographic record
Abstract
We study three classical problems of genome rearrangement--sorting, halving, and the median problem--in a restricted double cut and join (DCJ) model. In the DCJ model, introduced by Yancopoulos et al., we can represent rearrangement events that happen in multichromosomal genomes, such as inversions, translocations, fusions, and fissions. Two DCJ operations can mimic transpositions or block interchanges by first extracting an appropriate segment of a chromosome, creating a temporary circular chromosome, and then reinserting it in its proper place. In the restricted model, we are concerned with multichromosomal linear genomes and we require that each circular excision is immediately followed by its reincorporation. Existing linear-time DCJ sorting and halving algorithms ignore this reincorporation constraint. In this article, we propose a new algorithm for the restricted sorting problem running in O(n log n) time, thus improving on the known quadratic time algorithm. We solve the restricted halving problem and give an algorithm that computes a multilinear halved genome in linear time. Finally, we show that the restricted median problem is NP-hard as conjectured.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".