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Record W1989449643 · doi:10.3390/cancers4030846

A microRNA Link to Glioblastoma Heterogeneity

2012· article· en· W1989449643 on OpenAlexaff
Sanjay K. Singh, Alenoush Vartanian, Kelly Burrell, Gelareh Zadeh

Bibliographic record

VenueCancers · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsmicroRNAGlioblastomaPathogenesisBiologyCancer researchBioinformaticsMechanism (biology)NeuroscienceComputational biologyMedicineGeneGeneticsImmunology

Abstract

fetched live from OpenAlex

Glioblastomas (GBM) are one of the most malignant adult primary brain tumors. Through decades of research using various model systems and GBM patients, we have gained considerable insights into the mechanisms regulating GBM pathogenesis, but have mostly failed to significantly improve clinical outcome. For the most part GBM heterogeneity is responsible for this lack of progress. Here, we have discussed sources of cellular and microenvironmental heterogeneity in GBMs and their potential regulation through microRNA mediated mechanisms. We have focused on the role of individual microRNAs (miRNA) through their specific targets and miRNA mediated RNA-RNA interaction networks with the potential to influence various aspects of GBM heterogeneity including tumor neo-vascularization. We believe a better understanding of such mechanisms for regulation of GBM pathogenesis will be instrumental for future therapeutic options.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.266
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations18
Published2012
Admission routes1
Has abstractyes

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