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Record W1906899562 · doi:10.5376/cge.2015.03.0005

Epigenetic of Somatic Cells Reprogramming

2015· article· en· W1906899562 on OpenAlexvenueno aff
Jie Li, Yan Huang, Mingming Lu, Yihan Wang, Yan Zhang

Bibliographic record

VenueCancer Genetics and Epigenetics · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsnot available
Fundersnot available
KeywordsReprogrammingSomatic cellEpigeneticsBiologyEpigenesisCell biologyDNA methylationGeneticsGeneGene expression

Abstract

fetched live from OpenAlex

Embryonic stem cells (ESCs) is a highly undifferentiated pluripotent cells, can differentiate into various cells. Stem cell therapy is an opportunity of the human regenerative medicine, and also a focus of medical research. While the source of embryonic stem cells is controversial in ethics, which indirectly promotes the technology development of somatic cell reprogramming and induced pluripotent stem cells (iPSCs). Although iPSCs can produce healthy mature individuals, there are still some limitations, such as the success rate of iPSCs is very low and the problem of tumorigenicity. ESCs and iPSCs have difference in gene expression level and the potential mechanism of epigenetic regulation. In the process of cell differentiation and development, DNA sequence has no change, which means the specific epigenetic modifications (DNA methylation, histone modifications, miRNA and lncRNA) plays an important role in the pluripotent maintenance and differentiation of ESCs and iPSCs. In this review, we discussed the regulation effect of various epigenetic modifications in the pluripotent maintenance and differentiation of ESCs and iPSCs, and the relationship between epigenetic modifications and somatic reprogramming is also discussed. We think, based on the various omics data produced by next generation sequencing, developing bioinformatics methods will greatly promote the stem cell research and its clinical application.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.959

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 head0.037
GPT teacher head0.305
Teacher spread0.269 · 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 designBench or experimental
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

Citations0
Published2015
Admission routes1
Has abstractyes

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