Protein coding region prediction based on the adaptive representation method
Why this work is in the frame
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Bibliographic record
Abstract
This article proposes a new protein-coding-region prediction technique. The technique maps DNA sequences to numerical strings using an adaptive representation scheme and then uses signal processing to identify coding regions. We learn a mapping from symbols to numerical sequences by computing the distribution variance of each nucleotide in a DNA sequence, and then use the period-3 spectrum to distinguish coding and non-coding regions. Compared to other spectral methods, our method boosts the period-3 spectrum peaks in putative protein-coding regions and attenuates the extraneous peaks in putative non-coding regions by learning to weight the signal by the C-G to A-T ratios. Our adaptive representation method outperforms all other state-of-the-art spectral methods on every benchmark dataset available according to 3 different performance measures.
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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 it