Comparative genomic analysis using statistically optimal null filters
Why this work is in the frame
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Bibliographic record
Abstract
It is well established that the function of human gene can be identified by working on the corresponding gene in a model organism. Such comparative genomic studies have provided new insights into human biology and gene expression. Due to the explosion of genomic data in recent times, highly effective computational comparative genomic algorithms are in greater demand. In this research, a digital signal processing approach using statistically optimal null filter (SONF) is developed for comparative genomic analysis. The instantaneous matched filter in SONF determines the degree of local alignment between the genomic sequences being compared. Through examples the effectiveness of the proposed approach is illustrated in comparison with the other existing convolution based method. In particular, the proposed method is highly efficient in locating a short motif in a large genomic sequence.
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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) | 0.001 | 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