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Record W2040637273 · doi:10.1093/mutage/geu039

Arginine methyltransferases as novel therapeutic targets for breast cancer

2015· review· en· W2040637273 on OpenAlex
Alan Morettin, R. Michael Baldwin, Jocelyn Côté

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMutagenesis · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsMethyltransferaseBreast cancerProtein arginine methyltransferase 5CancerMethylationSignal transductionBiologyCancer researchDNA methylationBioinformaticsDNAGeneticsGene expressionGene

Abstract

fetched live from OpenAlex

Breast cancer is the most commonly diagnosed female cancer in the world. Though therapeutic treatments are available to treat breast cancer and in some instances are successful, the occurrence of unsuccessful treatment, or the rate of tumour recurrence, still remains strikingly high. Therefore, novel therapeutic treatment targets need to be discovered and tested. The protein arginine methyltransferases (PRMTs) are a family of enzymes that catalyse arginine methylation and are implicated in a myriad of cellular pathways including transcription, DNA repair, RNA metabolism, signal transduction, protein-protein interactions and subcellular localisation. In breast cancer, the expression levels and enzymatic activity of a number of PRMTs is dysregulated; significantly altering the regulation of many cellular pathways that are implicated in breast cancer development and progression. Here, we review the current knowledge on PRMTs in breast cancer and provide a rationale for how PRMTs may provide novel therapeutic targets for the treatment of breast cancer.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.052
GPT teacher head0.365
Teacher spread0.313 · 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