Polymorphisms Affecting Mirna Regulation: A New Level of Genetic Variation Affecting Disorders and Diseases of the Human Cns
Bibliographic record
Abstract
The recognition of people and/or populations at a high risk for the development of various types of neurological disorders and diseases is not only key to improved screening programs and earlier detection, but it also provides hope for appropriate treatment and care. Genetic alterations that change gene-expression levels have long been investigated for association with the development of pathological neurological conditions. Gene regulation by miRNAs is a relatively new area of study, and published evidence suggests that alterations in this process may be associated with increased disease risk. Here, the authors review the current data for association between single nucleotide polymorphisms (SNPs) and miRNA-mediated gene regulation (miR-SNPs) in human neuropsychiatric and neurodegenerative diseases. Additionally, we present an approach to detect and identify functional miR-SNPs for the purpose of carrying out large-scale genetic association studies. The growing body of literature suggests that miR-SNPs are emerging as a powerful tool, both to study the pathobiology of diseases, as well as aiding in its diagnosis and prognosis.
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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 | 0.000 | 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".