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Record W2620791330 · doi:10.1093/neuonc/nox106

miR miR on the wall, who’s the most malignant medulloblastoma miR of them all?

2017· review· de· W2620791330 on OpenAlexaff
Xin Wang, Borja Holgado, Vijay Ramaswamy, Stephen C. Mack, Kory Zayne, Marc Remke, Xiaochong Wu, Livia Garzia, Craig Daniels, Anna Marie Kenney, Michael D. Taylor

Bibliographic record

VenueNeuro-Oncology · 2017
Typereview
Languagede
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Cancer Institute
KeywordsMedulloblastomamicroRNABiologyCancerDiseaseCancer researchComputational biologyNeuroscienceBioinformaticsGeneGeneticsMedicinePathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.829
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.055
GPT teacher head0.326
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations25
Published2017
Admission routes1
Has abstractyes

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