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Record W2418646128 · doi:10.1385/1-59259-650-9:15

DNA Delivery to Cells in Culture: Generation of Adenoviral Libraries for High-Throughput Functional Screening

2004· article· en· W2418646128 on OpenAlexaff
M. Ogorelkova, Seyyed Mehdy Elahi, David Gagnon, Bernard Massie

Bibliographic record

VenueHumana Press eBooks · 2004
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsMontreal Clinical Research Institute
Fundersnot available
KeywordsBiologyComputational biologyProteomicsFunctional genomicsPeptide libraryGeneDNAPhage displayDNA sequencingProtein engineeringFunction (biology)RNAPeptideGeneticsGenomeGenomicsBiochemistryEnzymePeptide sequence

Abstract

fetched live from OpenAlex

In functional genomics, the use of expression libraries of DNA variants in combination with potent screening techniques is a powerful tool for gene discovery. They allow study of gene and protein function, generation of peptide variants with novel properties, as well as identification of functional short DNA and RNA motifs. In proteomics, generation of large expression libraries of protein variants with random substitutions ("directed evolution") and further screening for novel or improved functions has been commonly used for isolation of proteins with novel characteristics, for improving enzymes, for rapid isolation of antibodies, and for functional protein studies. Most commonly, peptide libraries are expressed and screened in prokaryotic systems. Such systems have the advantage of rapid and simple generation of clones expressing single variants, allow high diversity (up to 10(11)), and can be combined with phage- or cell-surface display technique (2). The main disadvantage of bacterial systems is the absence of posttranslational modifications and native folding of many mammalian proteins, leading to limited applications, particularly when enzyme-substrate-, protein-protein, or protein-RNA interactions are to be studied.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.147
GPT teacher head0.314
Teacher spread0.166 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations12
Published2004
Admission routes1
Has abstractyes

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