A predictive modeling approach for cell line-specific long-range regulatory interactions
Bibliographic record
Abstract
Nucl. Acids Res. 43 (18): 8694–8712. doi: 10.1093/nar/gkv865 The authors wish to make the following corrections to Figure 5: Evaluation of genome-wide enhancer-promoter interaction maps. (A) Shown is the distribution of normalized Hi-C contact count frequencies in genome-wide predictions for the H1hesc cell line. H1hesc-top: the interactions in the 90% confidence of the classifier trained using only H1hesc 5C data, H1hesc-bottom: interactions predicted at 10% confidence by the classifier trained only on the H1hesc data, percentile-top and percentile-bottom: Same as in H1hesc-top and bottom but using predictions from the percentile ensemble. PRESTIGE: interactions obtained from the PRESTIGE method, IMPET: interactions obtained from the IM-PET method. (B) Distribution of the number of interactions as a function of genomic distance using H1hesc-only classifier (RIPPLE H1hesc CV), Ensemble (RIPPLE H1hesc Ensemble), PRESTIGE and IMPET. (C) Fold enrichment of predicted interactions from RIPPLE, IMPET and PRESTIGE in experimental data sets of long-range interactions generated using ChIA-PET or high-resolution Hi-C. Each barplot shows a fold-enrichment measure of the number of recovered interactions of a particular type in the high confidence set of interactions. The RNA_PolII_1 data set is from Li et al., whereas the RNA_POLII_2 data set is from Heidari et al. All data sets other than Hires_Hi-C are ChIA-PET data sets. (D) Shown is the number of data sets for different cell lines (column) in which a method (row) was the best (highest fold enrichment) among the three methods compared. The greater the number the more often was a method ranked the best. In Figure 5B, the bottom panel is incorrectly labelled as RIPPLE. The correct method and label, as stated in the figure caption, should be IMPET. A corrected Figure is provided below. The results and conclusion of the article are not affected and remain valid. The authors apologise to the readers for the inconvenience caused.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".