Additional file 1 of Gene cascade analysis in human granulosa tumor cells (KGN) following exposure to high levels of free fatty acids and insulin
Bibliographic record
Abstract
Additional file 1: Supplemental Table S1. List of the first 100 upregulated DEGs in High fat + insulin (HFIns) treatment. Supplemental Table S2. List of the first 100 downregulated DEGs in High fat + insulin (HFIns) treatment. Supplemental Table S3. List of the first 100 upregulated DEGs in High fat (HF) treatment. Supplemental Table S4. List of the first 100 downregulated DEGs in High fat (HF) treatment. Supplemental Table S5. List of the first 100 upregulated DEGs in Insulin (INS) treatment. Supplemental Table S6. List of the first 100 downregulated DEGs in Insulin (INS) treatment. Supplemental Table S7. Complete list of significant (p-value ≤ 0,05) enriched canonical pathways of the differentially expressed genes using Ingenuity Pathway Analysis (IPA) software for insulin (INS) treatment. Supplemental Table S8. Complete list of significant (p-value ≤ 0,05) enriched canonical pathways of the differentially expressed genes using Ingenuity Pathway Analysis (IPA) software for high fat (HF) treatment. Supplemental Table S9. Complete list of significant (p-value ≤ 0,05) enriched canonical pathways of the differentially expressed genes using Ingenuity Pathway Analysis (IPA) software for high fat + insulin (HFIns) treatment. Supplemental Table S10. List of most significant upstream regulators in Insulin (INS) treatment (p-value of overlap ≤ 0,05). Supplemental Table S11. List of most significant upstream regulators in High fat (HF) treatment (p-value of overlap ≤ 0,05). Supplemental Table S12. List of most significant upstream regulators in High fat + insulin (HFIns) treatment (p-value of overlap ≤ 0,05).
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.748 | 0.137 |
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".