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Record W3033283870 · doi:10.1186/s13045-020-00894-2

PTENP1 is a ceRNA for PTEN: it’s CRISPR clear

2020· article· en· W3033283870 on OpenAlex
Marianna Vitiello, Monica Evangelista, Yang Zhang, Leonardo Salmena, Pier Paolo Pandolfi, Laura Poliseno

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.

Bibliographic record

VenueJournal of Hematology & Oncology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
FundersNational Institutes of HealthNational Cancer InstituteAssociazione Italiana per la Ricerca sul Cancro
KeywordsCompeting endogenous RNAPTENCRISPRPseudogeneBiologyComputational biologymicroRNAGeneGeneticsRNABioinformaticsPI3K/AKT/mTOR pathwayCancer researchLong non-coding RNAGenomeSignal transduction

Abstract

fetched live from OpenAlex

Here we apply state-of-the-art CRISPR technologies to study the impact that PTENP1 pseudogene transcript has on the expression levels of its parental gene PTEN, and hence on the output of AKT signaling in cancer. Our data expand the repertoire of approaches that can be used to dissect competing endogenous RNA (ceRNA)-based interactions, while providing further experimental evidence in support of the very first one that we discovered.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.774
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.037
GPT teacher head0.358
Teacher spread0.321 · 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