Nearby and non-nested genes in the human genome have more similar genotype tissue expression
Bibliographic record
Abstract
Neighboring genes within a shared promoter arrangement (i.e. opposite direction with the neighboring ends as the transcriptional start sites) are expected to have a high similarity in genotype tissue expression due to the potential overlap in the promoter region. This raises the question of whether similarity in expression profiles depends on orientation of the neighboring genes and whether there exist thresholds of locality where the similarity diminishes. Thus, in this work, we compared genotype tissue expression profiles at different genomic orientations and localities. Interestingly, there exist gene pairs in the human genome with very high or low expression similarity. Shorter chromosomes tend to have more similarly expressed genes. Also, a cluster of 3 adjacent genes within the average range of 20 to 60 kilobase pairs can have very similar expression profiles regardless of their orientations. However, when genes are nested and in opposite orientations, a lower than expected similarity was observed. Lastly, in cases where genotype tissue expression data does not exist or have low read counts (e.g. non-coding RNA), our identified influencing range can be a first estimate of the genotype tissue expression.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".