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Record W2411316645 · doi:10.1007/978-1-59745-304-2_10

Genotyping of Single Nucleotide Polymorphisms by Arrayed Primer Extension

2007· article· en· W2411316645 on OpenAlexaff
Scott J. Tebbutt

Bibliographic record

VenueMethods in molecular biology · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene expression and cancer classification
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPrimer extensionGenotypingOligonucleotideDNA microarrayComputational biologyBiologyGenotypeGeneticsNucleic acidPrimer (cosmetics)Single-nucleotide polymorphismNucleotideMolecular biologyDNAChemistryGeneGene expression

Abstract

fetched live from OpenAlex

Although the majority of microarray studies have been directed toward RNA expression profiling (functional genomics) and increasingly toward proteomics, a steady increase in the use of microarrays as platforms for DNA genotyping has occurred over the past 5 yr. Multiple array-based chemistries have been developed in order to genotype single nucleotide polymorphisms. Conceptually, the simplest of these microarray genotyping technologies is based on the dideoxynucleotide chemistry of mini-sequencing by arrayed primer extension, whereby oligonucleotide probes (preprinted on the array) are extended by a single nucleotide base. This enzyme-catalyzed single base extension reaction is dependent on the sequence (genotype) of the template nucleic acid (sample) that is temporarily hybridized to the probes. Utilization of all four dideoxynucleotides, each conjugated to a different fluorophore, allows genotyping by spectral differentiation of the single base extension reaction products.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.024
GPT teacher head0.360
Teacher spread0.336 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations3
Published2007
Admission routes1
Has abstractyes

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