Microarray data and pathway analyses for primary human activated hepatic stellate cells compared to HepG2 human hepatoma cells
Bibliographic record
Abstract
As nonalcoholic fatty liver disease progresses to end-stage diseases, including fibrosis, cirrhosis and hepatocellular carcinoma, fibrotic activated hepatic stellate cells and cancerous epithelial cells can become abundant, changing the cellular composition of this organ. Despite potentially residing within the same diseased tissue, direct comparisons of global gene expression between activated hepatic stellate cells and hepatocellular carcinoma cells are lacking. Here we provide data collected using Affymetrix GeneChip microarrays to identify differential gene expression in cultured primary human activated hepatic stellate cells compared to HepG2 human hepatoma cells. The dataset includes many genes involved in intermediary metabolism which were investigated in greater depth in our associated article (A.M. Hetherington, C.G. Sawyez, E. Zilberman, A.M. Stoianov, D.L. Robson, J.M. Hughes-Large, et al., 2016) [1]. Pathway analyses of known protein coding genes down-regulated or up-regulated by greater than 2.0-fold are also provided.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".