MétaCan
Menu
Back to cohort
Record W2068264637 · doi:10.2174/157339508784325073

Pleiotropic Roles of Runx Transcription Factors in the Differentiation and Function of T Lymphocytes

2008· article· en· W2068264637 on OpenAlexaff
Kazuyoshi Kohu, Masato Kubo, Hitoshi Ichikawa, Shin‐ichiro Ohno, Sonoko Habu, Takehito Sato, Masanobu Satake

Bibliographic record

VenueCurrent Immunology Reviews · 2008
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsInstitute of Aging
FundersFamily Process Institute
KeywordsTranscription factorEnhancerRUNX1BiologyTransgeneGeneCell biologyCellular differentiationGenetics

Abstract

fetched live from OpenAlex

The proteins of the Runx gene family are among the most important transcription factors for regulating the differentiation and function of T lymphocytes. Runx1 and Runx3 are each involved in multiple and distinct steps throughout the process of T-cell differentiation. Targeted disruption or transgenic overexpression of the Runx genes causes pleiotropic and pathological phenotypes, including cell differentiation arrest, abnormal growth or survival, and immunological disorders. Runx proteins exert positive or negative effects on the transcription of a variety of possible target genes, depending on the context of the promoter, enhancer, and silencer. We now have a basic understanding of Runx function. To fully understand T-lymphocyte differentiation and function, the next challenge will be to investigate how Runx, as a member of a regulatory network, works in cooperation with TCR/cytokine receptor signaling and other transcriptionrelated factors. Keywords: Runx, transcription factor, cell differentiation, gene targeting, transgene

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.912
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.066
GPT teacher head0.304
Teacher spread0.237 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations7
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Immunology ReviewsSame topicComputational Drug Discovery MethodsFrench-language works237,207