Comparative Genomics via Wavelet Analysis for Closely Related Bacteria
Bibliographic record
Abstract
Comparative genomics has been a valuable method for extracting and extrapolating genome information among closely related bacteria. The efficiency of the traditional methods is extremely influenced by the software method used. To overcome the problem here, we propose using wavelet analysis to perform comparative genomics. First, global comparison using wavelet analysis gives the difference at a quantitative level. Then local comparison using keto-excess or purine-excess plots shows precise positions of inversions, translocations, and horizontally transferred DNA fragments. We firstly found that the level of energy spectra difference is related to the similarity of bacteria strains; it could be a quantitative index to describe the similarities of genomes. The strategy is described in detail by comparisons of closely related strains: S.typhi CT18, S.typhi Ty2, S.typhimurium LT2, H.pylori 26695, and H.pylori J99.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".