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Record W120909144 · doi:10.1385/1-59259-879-x:305

Affymetrix Oligonucleotide Analysis of Gene Expression in the Injured Heart

2005· review· en· W120909144 on OpenAlexafffund
Bobby Yanagawa, Lydia Taylor, Theresa Deisher, Raymond Ng, George F. Schreiner, Timothy J. Triche, Decheng Yang, Bruce M. McManus

Bibliographic record

VenueHumana Press eBooks · 2005
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsSt. Paul's HospitalUniversity of British ColumbiaProvidence Health Care
FundersCanadian Institutes of Health Research
KeywordsDNA microarrayComputational biologyGene expressionMicroarrayOligonucleotideContext (archaeology)Gene chip analysisBiologyComplementary DNAGeneBioinformaticsGenetics

Abstract

fetched live from OpenAlex

Microarrays have helped researchers gain much insight into gene expression profiles in the context of many diseases including those in the injured heart. Our genomic investigations have been focused on elucidation of host gene responses to enterovirus infection. We have gained valuable technical expertise in using Affymetrix oligonucleotide arrays, also known as GeneChips, and cDNA spotted arrays to probe differential gene expression in both cultured cells and in heart tissue. Here, we provide a technique-focused supplement to the Affymetrix GeneChip Expression Analysis Manual for sample preparation, processing, and array hybridization. We provide expanded explanations to highlight important points within the existing protocol and offer variations to standard procedures when appropriate. For investigators using myocardial tissues for microarray experiments, we further address the necessity of and methods for in situ flushing of the vasculature, tissue homogenization, and considerations for limits of expression detection in rare cells. It is our intention to provide useful technical information, based on our experience, to assist those researchers using Affymetrix GeneChips in their own genomic research.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.066
GPT teacher head0.375
Teacher spread0.309 · 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

Citations5
Published2005
Admission routes2
Has abstractyes

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