Bibliographic record
Abstract
Human genetic experiments are often conducted based on the orthologous genes in other mammals such as mouse and rat. The resulting conclusions of such experiments are often limited in their applicability to the human situation. This has raised a question as to why the orthologous genes with closely related or even identical coding regions behave differently in various mammals, and motivated us to study the promoter of these genes. We proposed a functional promoter similarity index (FPSI) based on the number of putative, but statistically significant associations (p ≤ 0.05) between transcription factors and their target orthologous genes. We deduced such association through searching known transcription factor binding sites from promoters of the genes. The FPSI was validated using microarray gene expression data. We did pair-wise study of seven vertebrate genomes (human, chimpanzee, mouse, rat, dog, chicken, and zebrafish). The FPSIs of orthologous genes are generally high between human and chimpanzee, with a mean FPSI of 0.79, but gradually decrease when human is compared to the mouse (0.22), rat (0.2), dog (0.2), chicken (0.13) or zebrafish (0.06). We then performed an analogous analysis for 2128 human cancer-associated genes and the results were similar, but had significantly improved FPSIs between these human genes and their orthologs in mouse, rat, and dog. The differences in the promoter regions of orthologous genes appear to be genome wide and negatively correlated with divergence time of the organisms. Such correlation suggests that the FPSI could be used as a measure of phylogenetic conservation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".