Additional file 1 of Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet
Bibliographic record
Abstract
Additional file 1: Fig. S1. Stepwise epigenetic modification of cytosine. Fig. S2. Relationship between unmodified versus modified motif statistical significance of central enrichment (from CentriMo [53]) and modified base calling thresholds across different whole genome bisulfite sequencing (WGBS) and oxidative WGBS (oxWGBS) specimens, in mice [48]. Fig. S3. Relationship between unmodified versus modified ZFP57 statistical significance of central enrichment (from CentriMo [53]) and modified base calling thresholds across different WGBS and oxWGBS specimens, in mice [48]. Fig. S4. Relationship between unmodified versus modified C/EBPβ statistical significance of central enrichment (from CentriMo [53]) and modified base calling thresholds across different WGBS and oxWGBS specimens, in mice [48]. Fig. S5. ZFP57 (Strogantsev et al. [20] CB9; 56 142 ChIP-seq peaks) CentriMo analysis of de novo and JASPAR motifs (Methods). Fig. S6. CentriMo [53] results for OCT4 cleavage under targets and release using nuclease (CUT&RUN) in mouse embryonic stem cells (mESCs). Fig. S7. Modified versus unmodified motifs, combining score and cluster information, for a wide array of transcription factors.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.798 | 0.213 |
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".