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Record W2498066637 · doi:10.1007/978-1-59745-548-0_5

Constructing and Screening a cDNA Library

2008· article· en· W2498066637 on OpenAlexaff
Kevin Larade, Kenneth B. Storey

Bibliographic record

VenueMethods in molecular biology · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsCarleton University
Fundersnot available
KeywordsOpen reading frameENCODEcDNA libraryComplementary DNAGeneComputational biologyFrame (networking)BiologyReading (process)Key (lock)Computer scienceGeneticsPolitical scienceEcology

Abstract

fetched live from OpenAlex

Many organisms provide excellent models for studying disease states or for exploring the molecular adaptations that allow cells and organisms to cope with or survive different stresses. The construction of a cDNA library and subsequent screening for genes of interest allows researchers to select for genes that are likely to play key roles in the regulation or response to the condition or stress of interest, those that may not be expressed (or exist) in other systems. Determination of the open reading frame(s) of novel genes, and extensive analysis of the proteins they encode, can open up new avenues of research and promote intelligent design of downstream projects.

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)0.002
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 score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0040.004
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0100.019

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 head0.015
GPT teacher head0.312
Teacher spread0.297 · 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

Citations7
Published2008
Admission routes1
Has abstractyes

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