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Record W2475353358 · doi:10.1101/cshperspect.a026211

Attenuating the p53 Pathway in Human Cancers: Many Means to the Same End

2016· review· en· W2475353358 on OpenAlexfundno aff
Amanda R. Wasylishen, Guillermina Lozano

Bibliographic record

VenueCold Spring Harbor Perspectives in Medicine · 2016
Typereview
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsnot available
FundersNational Cancer InstituteCanadian Institutes of Health ResearchVedecká Grantová Agentúra MŠVVaŠ SR a SAV
KeywordsCancer researchMedicine

Abstract

fetched live from OpenAlex

The p53 pathway is perturbed in the majority of human cancers. Although this most frequently occurs through the direct mutation or deletion of p53 itself, there are a number of other alterations that can attenuate the pathway and contribute to tumorigenesis. For example, amplification of important negative regulators, MDM2 and MDM4, occurs in a number of cancers. In this work, we will review both the normal regulation of the p53 pathway and the different mechanisms of pathway inhibition in cancer, discuss these alterations in the context of the global genomic analyses that have been conducted across tumor types, and highlight the translational implications for cancer diagnosis and treatment.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.003

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 head0.047
GPT teacher head0.344
Teacher spread0.297 · 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 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

Citations134
Published2016
Admission routes1
Has abstractyes

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