Inference of Specific Gene Regulation by Environmental Chemicals in Human Embryonic Stem Cells
Bibliographic record
Abstract
We are exposed to many environmental chemicals in our daily life. Certain chemicals threaten our health, especially that of embryos and can cause serious developmental problems. To prevent abnormal development and diseases caused by chemicals, it is important to clarify the mechanisms of chemical toxicity in embryonic cells. The gene regulatory network is one of the useful methods for clarifying functional mechanisms in living cells, so we applied a statistical method to infer the gene regulatory network in human embryonic stem cells. In this study, we improved our previously developed SEM approach for inferring a network model from 9 gene expression profiles in human embryonic stem cells, which were exposed to various chemicals. The estimated regulatory models clarified the differences between chemicals, and the shapes of the inferred models reflected the features of the chemical toxicities. The toxicity of acrylamide affected neuronal cell-related genes, while that of diethylnitrosamine disturbed cell differentiation-related genes. On the other hand, the TCDD network reflected feedback regulation, and finally disturbed neuronal cell-related genes. In the Thalidomide network, cell differentiation genes related to axis formation in embyronic cells were affected by thalidomide toxicity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 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".