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Record W2103475704 · doi:10.1251/bpo114

Rapid and simple comparison of messenger rna levels using real-time PCR

2006· article· en· W2103475704 on OpenAlexafffund
Andrée‐Anne Dussault, Marc Pouliot

Bibliographic record

VenueBiological Procedures Online · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsUniversité Laval
FundersCanadian Arthritis NetworkCanadian Institutes of Health ResearchArthritis Society
KeywordsReal-time polymerase chain reactionComputational biologyMessenger RNAComputer sciencePolymerase chain reactionGeneNucleic acidBiologyBioinformaticsGenetics

Abstract

fetched live from OpenAlex

Real-time polymerase chain reaction (PCR) constitutes a significant improvement over traditional end-point PCR, as it allows the quantification of starting amounts of nucleic acid templates, in real-time. However, quantification requires validation through numerous internal controls and standard curves. We describe in this paper a simple protocol which uses real-time PCR to compare mRNA levels of a gene of interest between different experimental conditions. Comparative real-time PCR can be a relatively low-cost method and does not require sequence-specific fluorescent reporters. Moreover, several genes from a set of experiments can be assessed in a single run. Thus, in addition to providing a comparative profile for the expression of a gene of interest, this method can also provide information regarding the relative abundance of different mRNA species.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.004

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 head0.044
GPT teacher head0.336
Teacher spread0.292 · 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

Citations249
Published2006
Admission routes2
Has abstractyes

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