ADAPTIVE MACHINE LEARNING TECHNIQUE FOR PERIODICITY DETECTION IN BIOLOGICAL SEQUENCES
Bibliographic record
Abstract
Researchers devoted considerable effort to detect the periodicity in DNA sequences, namely, the DNA segments that wrap the Histone protein. It is anticipated that periodic dinucleotide signals are indicators of certain important spots (like binding regions) within a DNA sequence; they are equally spaced along nucleosomal DNA with approximately 10 base-pair period. Positioned nucleosomes are believed to play an important role in transcriptional regulation and for the organization of chromatin in cell nuclei. In this paper, we describe and apply a dynamic periodicity detection algorithm to discover the periodicity of certain dinucleotides in DNA and Protein sequences. Our algorithm is based on suffix tree as the underlying data structure. The proposed approach is suitable to analyze different kinds of data and can serve different targets. It considers the periodicity of alternative substrings like the three dinucleotides AA/TA/TT, in addition to considering dynamic window to detect the periodicity of certain instances of substrings. We tested the applicability, effectiveness and resilience of the proposed approach to noise as compared to the other existing algorithms described in the literature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".