Bibliographic record
Abstract
The data provided here are supplementary files for "Genome-Wide Histone Modifications and CTCF Enrichment Predict Gene Expression in Sheep Macrophages." Alisha T. Massa, Michelle R. Mousel, Maria K. Herndon, David R. Herndon, Brenda M Murdoch, Stephen N. White. Front. Genet., 07 January 2021. https://doi.org/10.3389/fgene.2020.612031 The compressed file contains BED format locations for promoters, enhancers, insulators, and silencer DNA regulatory elements as determined by ChIP-seq for H3K4me3, H3K27ac, H3K4me1, H3K27me3, and CTCF. Further metadata and protocol details are available on the FAANG data portal at https://data.faang.org/api/fire_api/assays/WSU_SOP_Native_ChIP-seq_Protocol_2019.pdf and https://data.faang.org/api/fire_api/analysis/WSU_SOP_ChIP-seq_Bioinformatic_Analysis_Protocol_2020.pdf This study is part of the FAANG project, promoting rapid prepublication of data to support the research community. These data are released under Fort Lauderdale principles, as confirmed in the Toronto Statement (Toronto International Data Release Workshop. Birney et al. 2009. Pre-publication data sharing. Nature 461:168-170). Any use of this dataset must abide by the FAANG data sharing principles. Data producers reserve the right to make the first publication of a global analysis of this data. If you are unsure if you are allowed to publish on this dataset, please contact the FAANG Data Coordination Centre and FAANG consortium (email faang-dcc@ebi.ac.uk and cc faang@iastate.edu) to enquire. The full guidelines can be found at http://www.faang.org/data-share-principle.
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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.839 | 0.379 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".