Additional file 1 of WACS: improving ChIP-seq peak calling by optimally weighting controls
Bibliographic record
Abstract
Additional file 1: Table 1. Table for the 45 ChIP-seq experiments and their corresponding ChIP-seq replicate samples and TFs for the K562 cell line from the ENCODE database used in our analysis. Table 2. Table for the 90 ChIP-seq samples and their corresponding control samples for the K562 cell line from the ENCODE database used in our analysis. Table 3. Table for the transcription factors (TFs) and their corresponding motif ID from JASPAR for 45 ChIP-seq experiments. Table 4. Table for the ChIP-seq experiments and their corresponding ChIP-seq replicate samples, TFs and controls for the A549 cell line from the ENCODE database used in our analysis. Table 5. Table for the ChIP-seq experiments and their corresponding ChIP-seq replicate samples, TFs and controls for the GM12878 cell line from the ENCODE database used in our analysis. Table 6. Table for the ChIP-seq experiments and their corresponding ChIP-seq replicate samples, TFs and controls for the HepG2 cell line from the ENCODE database used in our analysis. Table 7. Lab. Table 8. Year. Table 9. Mapped Read Length. Figure 1. Flowchart for the estimation of weights per control. Figure 2. Example of precision recall curve for TF ZNF24 ChIP-seq dataset ENCFF109OWW. Figure 3. AUPRC for the treatment samples. Figure 4. Histogram of the overall number of controls used per ChIP-seq dataset using WACS for 90 ChIP-seqs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.726 | 0.241 |
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".