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Record W1984878005 · doi:10.1002/stem.298

Distinguishing Between Mouse and Human Pluripotent Stem Cell Regulation: The Best Laid Plans of Mice and Men

2010· review· en· W1984878005 on OpenAlexaff
Angelique Schnerch, Chantal Cerdan, Mickie Bhatia

Bibliographic record

VenueStem Cells · 2010
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsBiologyEpigenomeInduced pluripotent stem cellTranscriptomeStem cellTranscription factorCell biologyComputational biologyTranscriptional regulationEmbryonic stem cellGeneticsCellular differentiationGene regulatory networkGeneGene expressionDNA methylation

Abstract

fetched live from OpenAlex

Pluripotent stem cells (PSCs) have been derived from the embryos of mice and humans, representing the two major sources of PSCs. These cells are universally defined by their developmental properties, specifically their self-renewal capacity and differentiation potential which are regulated in mice and humans by complex transcriptional networks orchestrated by conserved transcription factors. However, significant differences exist in the transcriptional networks and signaling pathways that control mouse and human PSC self-renewal and lineage development. To distinguish between universally applicable and species-specific features, we collated and compared the molecular and cellular descriptions of mouse and human PSCs. Here we compare and contrast the response to signals dictated by the transcriptome and epigenome of mouse and human PSCs that will hopefully act as a critical resource to the field. These analyses underscore the importance of accounting for species differences when designing strategies to capitalize on the clinical potential of human PSCs.

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)0.002
metaresearch head score (Gemma)0.001
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
Metaresearch0.0020.001
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 head0.034
GPT teacher head0.295
Teacher spread0.261 · 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

Citations91
Published2010
Admission routes1
Has abstractyes

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