Oct4 Targets Regulatory Nodes to Modulate Stem Cell
Bibliographic record
Abstract
Summary of Oct4 Correlated Genes with Probeset ID,gene symbol, gene name, direction and percentage of correlation,chromosomal location, summary GO category used for Figure 2and GO biological process were listed when known.Found at: doi:10.1371/journal.pone.0000553.s002 (0.28 MBXLS) Table S3 GoStat AnalysisFound at: doi:10.1371/journal.pone.0000553.s003 (0.33 MBXLS) Table S4 Oct4/Sox2 putative binding site analysis with Genesymbol, RefSeq or Ensembl ID, putative binding sequence, andlocation in transcript enumeratedFound at: doi:10.1371/journal.pone.0000553.s004 (0.11 MBXLS) Table S5 Primer sequences for Oct4 target validation by ChIP/QRT-PCRFound at: doi:10.1371/journal.pone.0000553.s005 (0.06 MBDOC) Table S6 Annotation of Oct4 targets.Found at: doi:10.1371/journal.pone.0000553.s006 (0.09 MBDOC) ACKNOWLEDGMENTS The Authors would like to acknowledge the Stem Cell Network for theirsupport of the Stem Cell Genomics Project and the technical staff of theOntario Genomics Innovation Centre for their expert assistance. Kindthanks to Dave Picketts, Marjorie Brand, and Jeff Dilworth for insightfuldiscussions. MAR is an International Scholar of the Howard HughesMedical Institute and holds the Canada Research Chair in MolecularGenetics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.011 |
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".