Bridging in vivo and in vitro data from Japanese Toxicogenomics Project using network analyses
Bibliographic record
Abstract
Since experiments involving animal models are labor and time intensive, there is an attempt to replace these measurements on animal models with in vitro assays which has higher acceptance in the population concerning ethical issues. In this work, we explore to what extend animal models can be replaced by in vitro assays in the context of a toxicogenomics study. The data from the Japanese Toxicogenomics Project are gene expression profiles measured by microarrays from both in vitro and animal samples. We apply a comprehensive genomic association network analysis in order to study the comparative behavior of the genomic networks for the in vivo vs. in vitro data. The genomic networks are computed based on association scores of gene-gene pairs using a partial least squares modeling of gene expression values adjusted for sacrifice time and dosage. We apply permutation based statistical tests to compare the connectivity of a given gene, as well as a class of genes in the two networks which may be affected by a given drug. The goal is to identify parts of these networks including key genes that are not significantly altered for in vivo vs. in vitro samples for the majority of the drugs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".