<i>ZMAT2</i> in Humans and Other Primates: A Highly Conserved and Understudied Gene
Bibliographic record
Abstract
Recent advances in genetics present unique opportunities for enhancing our understanding of human physiology and disease predisposition through detailed analysis of gene structure, expression, and population variation via examination of data in publicly accessible genome and gene expression repositories. Yet, the vast majority of human genes remain understudied. Here, we show the scope of these genomic and genetic resources by evaluating ZMAT2, a member of a 5-gene family that through May 2020 had been the focus of only 4 peer-reviewed scientific publications. Using analysis of information extracted from public databases, we show that human ZMAT2 is a 6-exon gene and find that it exhibits minimal genetic variation in human populations and in disease states, including cancer. We further demonstrate that the gene and its encoded protein are highly conserved among nonhuman primates and define a cohort of ZMAT2 pseudogenes in the marmoset genome. Collectively, our investigations illustrate how complementary use of genomic, gene expression, and population genetic resources can lead to new insights about human and mammalian biology and evolution, and when coupled with data supporting key roles for ZMAT2 in keratinocyte differentiation and pre-RNA splicing argue that this gene is worthy of further study.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".