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Record W2427162414 · doi:10.1007/978-1-61779-931-0_2

Recombinant Antibodies and In Vitro Selection Technologies

2012· review· en· W2427162414 on OpenAlexaff
C. Ronald Geyer, John McCafferty, Stefan Dübel, Andrew Bradbury, Sachdev S. Sidhu

Bibliographic record

VenueMethods in molecular biology · 2012
Typereview
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsUniversity of TorontoUniversity of Saskatchewan
Fundersnot available
KeywordsPhage displayComputational biologyAntibodyHybridoma technologyBiologyFunction (biology)Computer scienceSelection (genetic algorithm)Monoclonal antibodyGeneticsArtificial intelligence

Abstract

fetched live from OpenAlex

Over the past decade, the accumulation of detailed knowledge of antibody structure and function has enabled antibody phage display to emerge as a powerful in vitro alternative to hybridoma methods for creating antibodies. Many antibodies produced using phage display technology have unique properties that are not obtainable using traditional hybridoma technologies. In phage display, selections are performed under controlled, in vitro conditions that are tailored to suit demands of the antigen and the sequence encoding the antibody is immediately available. These features obviate many of the limitations of hybridoma methodology, and because the entire process relies on scalable molecular biology techniques, phage display is also suitable for high-throughput applications. Thus, antibody phage display technology is well suited for genome-scale biotechnology and therapeutic applications. This review describes the antibody phage display technology and highlights examples of antibodies with unique properties that cannot easily be obtained by other technologies.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.096
GPT teacher head0.498
Teacher spread0.402 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations72
Published2012
Admission routes1
Has abstractyes

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