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Record W2899655237 · doi:10.3390/cancers10110422

Targeting the Hippo Pathway for Breast Cancer Therapy

2018· review· en· W2899655237 on OpenAlex
Liqing Wu, Xiaolong Yang

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

VenueCancers · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHippo pathway signaling and YAP/TAZ
Canadian institutionsQueen's University
FundersCanadian Institutes of Health ResearchCanadian Cancer Society
KeywordsHippo signaling pathwaySuppressorBreast cancerCancer researchMedicineTargeted therapyCancerBioinformaticsBiologySignal transductionInternal medicineCell biology

Abstract

fetched live from OpenAlex

Breast cancer (BC) is one of the most prominent diseases in the world, and the treatments for BC have many limitations, such as resistance and a lack of reliable biomarkers. Currently the Hippo pathway is emerging as a tumor suppressor pathway with its four core components that regulate downstream transcriptional targets. In this review, we introduce the present targeted therapies of BC, and then discuss the roles of the Hippo pathway in BC. Finally, we summarize the evidence of the small molecule inhibitors that target the Hippo pathway, and then discuss the possibilities and future direction of the Hippo-targeted drugs for BC therapy.

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 score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.040
GPT teacher head0.323
Teacher spread0.283 · 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