A New, Dynamic-Representation-Based Gene Finding Method with an Analysis of False Positive Peaks
Bibliographic record
Abstract
In this paper, we propose a new method for gene finding. The method uses a new dynamic representation scheme to map DNA sequences into a numerical form. The dynamic representation scheme assigns numerical pairs to the nucleotides based on their effectiveness in the period-3 spectrum. Nucleotides that have a stronger participation in the period-3 spectrum peaks are assigned numerical pairs that further enhance their participation. Another development that the proposed method introduces is the detection of the period-3 spectrum peaks to discriminate between protein coding and non-coding regions. In this paper, we also analyze the period-3 peaks that are predicted by the proposed method. We analyze the false positive peaks by scanning the stop codons in the possible reading frames. The work also analyzes the false positive peaks that are attached to true positive peaks. This analysis provides insights for future work that can be conducted to improve the prediction accuracy of spectrum-based techniques by studying the false positive peaks. In addition, it provides an insight about these false positive peaks that may have originated as transcribed sequences which, over time, acquired stop codons by mutation and lost their characteristic for transcription.
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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 | 0.001 |
| 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".