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Record W2108134720 · doi:10.1186/gm441

Protein-protein interaction networks: probing disease mechanisms using model systems

2013· review· en· W2108134720 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.

Bibliographic record

VenueGenome Medicine · 2013
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioinformatics and Genomic Networks
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProteomicsProtein–protein interactionComputational biologySystems biologyDiseaseHuman geneticsProtein Interaction NetworksBioinformaticsBiologyMedicineGeneticsPathologyGene

Abstract

fetched live from OpenAlex

Protein-protein interactions (PPIs) and multi-protein complexes perform central roles in the cellular systems of all living organisms. In humans, disruptions of the normal patterns of PPIs and protein complexes can be causative or indicative of a disease state. Recent developments in the biological applications of mass spectrometry (MS)-based proteomics have expanded the horizon for the application of systematic large-scale mapping of physical interactions to probe disease mechanisms. In this review, we examine the application of MS-based approaches for the experimental analysis of PPI networks and protein complexes, focusing on the different model systems (including human cells) used to study the molecular basis of common diseases such as cancer, cardiomyopathies, diabetes, microbial infections, and genetic and neurodegenerative disorders.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.750
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.044
GPT teacher head0.293
Teacher spread0.249 · 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