miR miR on the wall, who’s the most malignant medulloblastoma miR of them all?
Bibliographic record
Abstract
microRNAs (miRNAs) have wide-ranging effects on large-scale gene regulation. As such, they play a vital role in dictating normal development, and their aberrant expression has been implicated in cancer. There has been a large body of research on the role of miRNAs in medulloblastoma, the most common malignant brain tumor of childhood. The identification of the 4 molecular subgroups with distinct biological, genetic, and transcriptional features has revolutionized the field of medulloblastoma research over the past 5 years. Despite this, the growing body of research on miRNAs in medulloblastoma has largely focused on the clinical entity of a single disease rather than the molecular subgroups. This review begins by highlighting the role of miRNAs in development and progresses to explore their myriad of implications in cancer. Medulloblastoma is characterized by increased proliferation, inhibition of apoptosis, and maintenance of stemness programs-features that are inadvertently regulated by altered expression patterns in miRNAs. This review aims to contextualize the large body of work on miRNAs within the framework of medulloblastoma subgroups. The goal of this review is to stimulate new areas of research, including potential therapeutics, within a rapidly growing field.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".