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Record W2619799161 · doi:10.1016/j.ebiom.2017.05.027

Nuclear Factor I/B: A Master Regulator of Cell Differentiation with Paradoxical Roles in Cancer

2017· review· en· W2619799161 on OpenAlexafffund
Daiana D. Becker‐Santos, Kim M. Lonergan, Richard M. Gronostajski, Wan L. Lam

Bibliographic record

VenueEBioMedicine · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChromatin Remodeling and Cancer
Canadian institutionsUniversity of British ColumbiaOccupational Cancer Research Centre
FundersCanadian Institutes of Health ResearchNew York State Stem Cell Science
KeywordsTranscription factorCancerContext (archaeology)Cancer researchMaster regulatorBiologyMedicineGeneticsGene

Abstract

fetched live from OpenAlex

Emerging evidence indicates that nuclear factor I/B (NFIB), a transcription factor required for proper development and regulation of cellular differentiation in several tissues, also plays critical roles in cancer. Despite being a metastatic driver in small cell lung cancer and melanoma, it has become apparent that NFIB also exhibits tumour suppressive functions in many malignancies. The contradictory contributions of NFIB to both the inhibition and promotion of tumour development and progression, corroborates its diverse and context-dependent roles in many tissues and cell types. Considering the frequent involvement of NFIB in cancer, a better understanding of its multifaceted nature may ultimately benefit the development of novel strategies for the management of a broad spectrum of malignancies. Here we discuss recent findings which bring to light NFIB as a crucial and paradoxical player in 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.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.056
GPT teacher head0.342
Teacher spread0.286 · 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

Citations74
Published2017
Admission routes2
Has abstractyes

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