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Record W1980538968 · doi:10.1002/cplx.20100

Modeling pathways of differentiation in genetic regulatory networks with Boolean networks

2005· article· en· W1980538968 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComplexity · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene Regulatory Network Analysis
Canadian institutionsUniversity of Calgary
FundersNational Institutes of HealthNational Science Foundation
KeywordsAttractorObservableCellular differentiationNonlinear systemPerturbation (astronomy)Computer scienceGene regulatory networkVariety (cybernetics)GeneTopology (electrical circuits)BiologyBiological systemMathematicsPhysicsGeneticsGene expressionArtificial intelligenceMathematical analysis

Abstract

fetched live from OpenAlex

We have carried out the first examination of pathways of cell differentiation in model genetic networks in which cell types are assumed to be attractors of the nonlinear dynamics, and differentiation corresponds to a transition of the cell to a new basin of attraction, which may be induced by a signal or noise perturbation. The associated flow along a transient to a new attractor corresponds to a pathway of differentiation. We have measured a variety of features of such model pathways of differentiation, most of which should be observable using gene array techniques. © 2005 Wiley Periodicals, Inc. Complexity 11: 52–60, 2005

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.755

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 head0.021
GPT teacher head0.219
Teacher spread0.198 · 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