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Record W3080547630 · doi:10.1016/j.dib.2020.106229

Methylation data of mouse Rb-deficient pineoblastoma

2020· article· en· W3080547630 on OpenAlexafffund
Philip E.D. Chung, Eldad Zacksenhaus

Bibliographic record

VenueData in Brief · 2020
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health Research
KeywordsMethylationEpigeneticsDNA methylationBiologyBrain tumorCancer researchComputational biologyPathologyGeneticsMedicineGeneGene expression

Abstract

fetched live from OpenAlex

Methylation profiling is widely used to study tumor biology and perform cluster analysis, particularly in brain cancer research where tissue biopsies are scarce. We have recently reported on the development of novel mouse models for germ line mutations in pineoblastoma (Nature Communications, 2020). Here, we present unpublished methylation profiling of 8 Rb-deleted/p53-deleted pineoblastoma from our mouse model as well as 3 normal cerebellum tissues as control. The primary dataset can be accessed via SRA (PRJNA638504). These methylation data can be used to perform inter- and intra-species comparisons with other brain cancers as well as with specific subtypes of pineoblastoma, and to investigate potential epigenetic mechanisms and pathways underlying Rb-deficient pineoblastoma-genesis..

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

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

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 head0.117
GPT teacher head0.338
Teacher spread0.221 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2020
Admission routes2
Has abstractyes

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