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Record W1965171930 · doi:10.1038/nmeth.2895

The mammalian-membrane two-hybrid assay (MaMTH) for probing membrane-protein interactions in human cells

2014· article· en· W1965171930 on OpenAlexafffund
Julia Petschnigg, Bella Groisman, Max Kotlyar, Mikko Taipale, Yong Zheng, Christoph F. Kurat, Azin Sayad, J. Rafael Sierra, Mojca Mattiazzi Ušaj, Jamie Snider, Alex Nachman, Irina Krykbaeva, Ming‐Sound Tsao, Jason Moffat, Tony Pawson, Susan Lindquist, Igor Jurišica, Igor Štagljar

Bibliographic record

VenueNature Methods · 2014
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaHoward Hughes Medical InstituteAustrian Science FundOntario Genomics InstituteCanadian Institutes of Health ResearchOntario GenomicsUniversity of Toronto
KeywordsCell biologyUbiquitinBiologyMembrane proteinSignal transductionPhosphorylationIntegral membrane proteinCell membraneCell surface receptorCellMembraneBiochemistryGene

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.005

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 head0.035
GPT teacher head0.433
Teacher spread0.398 · 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

Citations184
Published2014
Admission routes2
Has abstractno

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