Algorithms to Reconstruct the Target Dna from Its Spectrum Connected at Some Level
Bibliographic record
Abstract
In order to sequence a target DNA, it is first cleaved into many shorter overlapping fragments by restriction enzymes. Then each such a short fragment is identified as a letter string over the alphabet {A,C,G,T}called a read fragment. We call the set of all read fragments which covers the target DNA a spectrum. It is believe that the shortest superstring of its spectrum outlines very well the target DNA. Unfortunately, the problem of finding the shortest superstring for any given set of strings S is NP-hard. However, in the biologically meaningful cases, the problem needn’t be so hard. An observation is that it is not convincible that two read fragments consisting of several hundred letters, which come from consecutive locations on the target DNA, have only overlap of several letters. From this observation, one may reasonably assume that strings in the spectrum have enough overlap (connectivity). A class of important instances satisfying this assumption are those whose spectrum is from DNA array. Based on this assumption and another about repeat, the main result in the presented paper is: if the spectrum S of a target DNA is substring-free and connected at level t , and the target DNA has no repeats of size t or larger, then there exist an algorithm to reconstruct the target DNA in O( S ) .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".