Semiparametric Tests for Identifying Differentially Methylated Loci With Case–Control Designs Using Illumina Arrays
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
DNA methylation plays an important role in the development of many types of cancer. Identifying differentially methylated loci between cancer and normal patients is one of the central tasks to understand the contributions of the methylation process on cancer development. Through investigation of the methylation measurements generated by the Illumina methylation arrays, we notice that the methylation measurements of the cancer and normal groups could differ not only in means but also in variances. Therefore, we propose a generalized exponential tilt model to capture the differences in both means and variances between the cancer and normal groups. We derive the semiparametric tests to obtain model robustness. Through simulation studies, we demonstrate the feasibility of the proposed tests and a much improved power of the proposed tests than that of the t-test and the regression-based tests when the cancer and normal groups are different in variances only or in both means and variances. Hence the proposed tests can serve as useful complements to the standard tests that only test differences in means. We also illustrate the proposed methods by applying to a real methylation data from a recent study on ovarian cancer where the proposed methods identified additional methylation loci that were missed by the existing method.
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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 | 0.001 |
| 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