Comparison of Machine Learning and Pattern Discovery Algorithms for the Prediction of Human Single Nucleotide Polymorphisms
Why this work is in the frame
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Bibliographic record
Abstract
This paper compares machine learning techniques and pattern discovery algorithms for the prediction of human single nucleotide polymorphisms (SNPs). We selected six pattern discovery algorithms (YMF, Projection, Weeder, MotifSampler, AlignACE and ANN-Spec) and two machine learning techniques (Random Forests and K-Nearest Neighbours) and applied them to the DNA sequences flanking non- coding SNPs on human chromosome 21. We compared the pattern similarity amongst the methods and validated the predictions using known SNPs on chromosome 22. Parameterization of both machine learning and pattern discovery algorithms was critical to their performance. Memory usage was broadly constant amongst the pattern discovery algorithms, but the CPU running time varied significantly between deterministic and probabilistic pattern discovery methods, i.e., on average, probabilistic methods run19 times slower than deterministic methods. This is the first demonstration of SNP prediction, as well as the first comparison of machine learning and pattern discovery algorithms in SNP prediction studies.
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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 | 0.001 | 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