Gene expression analysis of livers from female B6C3F1 mice exposed to carcinogenic and non-carcinogenic doses of furan, with or without bromodeoxyuridine (BrdU) treatment
Bibliographic record
Abstract
Standard methodology for identifying chemical carcinogens is both time-consuming and resource intensive. Researchers are actively investigating how new technologies can be used to identify chemical carcinogens in a more rapid and cost-effective manner. Here we performed a toxicogenomic case study of the liver carcinogen furan. Full study and mode of action details were previously published in the Journal of Toxicology and Applied Pharmacology. Female B6C3F1 mice were sub-chronically treated with two non-carcinogenic (1 and 2 mg/kg bw) and two carcinogenic (4 and 8 mg/kg bw) doses of furan for 21 days. Half of the mice in each dose group were also treated with 0.02% bromodeoxyuridine (BrdU) for five days prior to sacrifice [13]. Agilent gene expression microarrays were used to measure changes in liver gene and long non-coding RNA expression (published in Toxicological Sciences). Here we describe the experimental and quality control details for the microarray data. We also provide the R code used to analyze the raw data files, produce fold change and false discovery rate (FDR) adjusted p values for each gene, and construct hierarchical clustering between datasets.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".