Prediction of Protein Coding Regions Using a Wide-Range Wavelet Window Method
Why this work is in the frame
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Bibliographic record
Abstract
Prediction of protein coding regions is an important topic in the field of genomic sequence analysis. Several spectrum-based techniques for the prediction of protein coding regions have been proposed. However, the outstanding issue in most of the proposed techniques is that these techniques depend on an experimentally-selected, predefined value of the window length. In this paper, we propose a new Wide-Range Wavelet Window (WRWW) method for the prediction of protein coding regions. The analysis of the proposed wavelet window shows that its frequency response can adapt its width to accommodate the change in the window length so that it can allow or prevent frequencies other than the basic frequency in the analysis of DNA sequences. This feature makes the proposed window capable of analyzing DNA sequences with a wide range of the window lengths without degradation in the performance. The experimental analysis of applying the WRWW method and other spectrum-based methods to five benchmark datasets has shown that the proposed method outperforms other methods along a wide range of the window lengths. In addition, the experimental analysis has shown that the proposed method is dominant in the prediction of both short and long exons.
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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