Additional file 2 of Comprehensive enhancer-target gene assignments improve gene set level interpretation of genome-wide regulatory data
Bibliographic record
Abstract
Additional file 2: Table S1: Overview of the top 19 EnTDefs, including the ranks, enhancer/enhancer-gene link methods, and basic summary statistics. Table S2: The 31 ENCODE ChIP-seq datasets from 9 completely different cell lines and 14 completely different transcription factors. Table S3: The nine ChIA-PET datasets used for generating cell-type-specific EnTDefs (CT-EnTDefs) and number of TFs assayed by ENCODE ChIP-seq in each particular cell type, which were used to evaluate the performance of the CT-EnTDefs. Table S4: Overview of the seven independent datasets used for the comparative analysis. Table S5: ChIA-PET datasets used by “ChIA” and “Loop” methods to assign enhancer to target genes in a cell-type independent manner (general EnTDefs). Table S6: The 87 ENCODE ChIP-seq datasets used for EnTDef evaluation (evaluation ChIP-seq) (tab 1) and the TF vs. cell type matrix (tab 2). Table S7: The 13 ENCODE ChIP-seq datasets from 4 different cell lines (testing ChIP-seq).
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.500 | 0.147 |
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".