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Record W1982717186 · doi:10.1039/c3mb70135b

High throughput strategies for probing the different organizational levels of protein interaction networks

2013· review· en· W1982717186 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMolecular BioSystems · 2013
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioinformatics and Genomic Networks
Canadian institutionsUniversity of British Columbia
FundersDanish Agency for Science and Higher EducationCanadian Institutes of Health ResearchMokslo, Inovaciju ir Technologiju AgenturaCanada Research Chairs
KeywordsProtein–protein interactionProtein Interaction NetworksContext (archaeology)Computer scienceComputational biologySystems biologyInteraction networkThroughputScale (ratio)BiologyData scienceGeneticsGeneTelecommunications

Abstract

fetched live from OpenAlex

Most proteins do not exist as isolated molecules in the cell, but instead serve as nodes of protein interaction networks. A number of techniques have been developed in the last two decades to study protein interaction networks at different levels of detail. Here we describe some of the techniques for characterizing protein interactions and protein complexes on a system-wide scale, focusing especially on newly emerging techniques that use co-migration. These newer approaches have the advantage that no genetic manipulation is necessary, thereby allowing investigation of protein complexes at their endogenous levels in the correct cellular context. Finally, we discuss different approaches for measuring large-scale temporal changes to protein interaction networks, an area that we believe will be one of the frontiers in systems biology in the coming years.

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.

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 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 score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.025
GPT teacher head0.271
Teacher spread0.246 · 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