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Record W2769276755 · doi:10.1016/j.semcdb.2017.11.010

The myogenic regulatory factors, determinants of muscle development, cell identity and regeneration

2017· review· en· W2769276755 on OpenAlexafffund
J. Manuel Hernández‐Hernández, Estela G. García-González, Caroline Brun, Michael A. Rudnicki

Bibliographic record

VenueSeminars in Cell and Developmental Biology · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesCanadian Institutes of Health ResearchStem Cell NetworkMuscular Dystrophy Association
KeywordsMYF5MyoDMyogeninMyogenesisMyogenic regulatory factorsBiologyTranscription factorCell biologyMyoD ProteinRegeneration (biology)GeneticsMyocyteGene

Abstract

fetched live from OpenAlex

The Myogenic Regulatory Factors (MRFs) Myf5, MyoD, myogenin and MRF4 are members of the basic helix-loop-helix family of transcription factors that control the determination and differentiation of skeletal muscle cells during embryogenesis and postnatal myogenesis. The dynamics of their temporal and spatial expression as well as their biochemical properties have allowed the identification of a precise and hierarchical relationship between the four MRFs. This relationship establishes the myogenic lineage as well as the maintenance of the terminal myogenic phenotype. The application of genome-wide technologies has provided important new information as to how the MRFs function to activate muscle gene expression. Application of combined functional genomics technologies along with single cell lineage tracing strategies will allow a deeper understanding of the mechanisms mediating myogenic determination, cell differentiation and muscle regeneration.

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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.005

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 head0.036
GPT teacher head0.314
Teacher spread0.278 · 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

Citations675
Published2017
Admission routes2
Has abstractyes

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