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Record W2287398810 · doi:10.1007/978-1-60327-148-6_6

Clinical Uses of Microarrays in Cancer Research

2008· review· en· W2287398810 on OpenAlexafffund
Carl Virtanen, James R. Woodgett

Bibliographic record

VenueMethods in molecular medicine · 2008
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene expression and cancer classification
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalDiscovery CentreUniversity Health Network
FundersCanadian Institutes of Health Research
KeywordsMicroarrayDNA microarrayAnnotationGene chip analysisSoftwareMicroarray analysis techniquesCancerComputational biologyComputer scienceGeneBioinformaticsBiologyGeneticsGene expressionProgramming language

Abstract

fetched live from OpenAlex

Perturbations in genes play a key role in the pathogenesis of cancer. Microarray-based technology is an ideal way in which to study the effects and interactions of multiple genes in cancer. There are many technologic challenges in running a microarray study, including annotation of genes likely to be involved, designing the appropriate experiment, and ensuring adequate quality assurance steps are implemented. Once data are normalized, they need to be analyzed; and for this, there are numerous software packages and approaches.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.981
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.361
GPT teacher head0.634
Teacher spread0.274 · 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 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

Citations27
Published2008
Admission routes2
Has abstractyes

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