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Record W1909444051 · doi:10.1002/0471140864.ps2119s56

The CLIP‐CHIP Oligonucleotide Microarray: Dedicated Array for Analysis of All Protease, Nonproteolytic Homolog, and Inhibitor Gene Transcripts in Human and Mouse

2009· article· en· W1909444051 on OpenAlexafffund
Reinhild Kappelhoff, Christopher M. Overall

Bibliographic record

VenueCurrent Protocols in Protein Science · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA regulation and disease
Canadian institutionsUniversity of British Columbia
FundersCanadian Breast Cancer Research AllianceCanada Research ChairsBreast Cancer Alliance
KeywordsComplementary DNAProteasesMolecular biologyOligonucleotideRNABiologyDNA microarrayMessenger RNAReverse transcriptaseProteaseGeneGene expressionRNA extractionBiochemistryEnzyme

Abstract

fetched live from OpenAlex

The CLIP-CHIP oligonucleotide microarray allows the analysis of mRNA transcript levels in a tissue sample for all proteases, nonproteolytic homologs, and protease inhibitors of the human and mouse genome. In the protocol presented in this unit, total RNA is extracted from a tissue, and the resulting mRNA is reverse transcribed into cDNA and dsDNA and then amplified in an in vitro transcription reaction. The amplified antisense RNA is labeled with a fluorescent dye and hybridized to the CLIP-CHIP, which contains unique oligonucleotides that are specifically designed for the protease, nonproteolytic homologs, protease inhibitors, and control samples. After hybridization, the fluorescence intensity of each spot is measured, thus identifying mRNA transcripts that are expressed and allowing basic quantification of expressed transcripts.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.031
GPT teacher head0.348
Teacher spread0.317 · 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

Citations9
Published2009
Admission routes2
Has abstractyes

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