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Record W4396909065 · doi:10.30919/es1152

s Generalization of Gene Network Representation on the Hypercube

2024· article· en· W4396909065 on OpenAlex
Pabel Shahrear, Ummey Habiba, Shajedul Karim, Rezwan Shahrear

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

VenueEngineered Science · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene Regulatory Network Analysis
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsHypercubeGeneralizationRepresentation (politics)Relation (database)Dimension (graph theory)Theoretical computer scienceComputer scienceMatrix (chemical analysis)Connection (principal bundle)State (computer science)VisibilityDiscrete mathematicsAlgorithmMathematicsCombinatoricsParallel computingData mining

Abstract

fetched live from OpenAlex

This article emphasizes the relation between Boolean input variables and Boolean states since the complexity of such connectivity increases enormously.Graphically, genetic systems up to 4-dimensional states of the implementation on hypercubes are accessible because the visibility of genetic systems up to 4-dimension on a hypercube is not laborious.The state connection on a hypercube is inflexible and only possible if the input variables are higher or more significant, for example, N 6.We have explored similar relations in this manuscript for higher dimensions.An algorithm is developed in the form of a matrix such that the connections of higher dimensional genetic networks are understandable on the hypercube.We have obtained the resultant output matrix based on the linear fractional maps, which are indispensable to understanding the system's behavior.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.245
Teacher spread0.234 · 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