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Record W2891279096 · doi:10.1101/411355

Limited significance of the in situ proximity ligation assay

2018· preprint· en· W2891279096 on OpenAlexaff
Azam Alsemarz, Paul Lasko, François Fagotto

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2018
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcGill University
FundersLaboratoire d'Excellence EpiGenMedAgence Nationale de la Recherche
KeywordsProximity ligation assayIn situLigationComputational biologyResolution (logic)AntibodyMacromoleculeScale (ratio)ChemistryBiologyBiological systemComputer scienceBiophysicsMolecular biologyPhysicsBiochemistryArtificial intelligenceImmunology

Abstract

fetched live from OpenAlex

Summary In situ proximity ligation assay (isPLA) is an increasingly popular technique that aims at detecting the close proximity of two molecules in fixed samples using two primary antibodies. The maximal distance between the antibodies required for producing a signal is 40 nm, which is lower than optical resolution and approaches the macromolecular scale. Therefore, isPLA may provide refined positional information, and is commonly used as supporting evidence for direct or indirect protein-protein interaction. However, we show here that this method is inherently prone to false interpretations, yielding positive and seemingly ‘specific’ signals even for totally unrelated antigens. We discuss the difficulty to produce adequate specificity controls. We conclude that isPLA data should be considered with extreme caution.

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)0.015
metaresearch head score (Gemma)0.057
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: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.057
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0030.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.004

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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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
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

Citations26
Published2018
Admission routes1
Has abstractyes

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