MétaCan
Menu
Back to cohort
Record W2008936042 · doi:10.1002/qsar.200860108

The Use of Sequence‐Derived QSPR Descriptors for Predicting Highly Connected Proteins (Hubs) in Protein–Protein Interactions

2009· article· en· W2008936042 on OpenAlex
Kendall Byler, Michael Hsing, Artem Cherkasov

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

VenueQSAR & Combinatorial Science · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsUniversity of British Columbia
FundersGenome British ColumbiaGenome Canada
KeywordsQuantitative structure–activity relationshipComputational biologySequence (biology)Staphylococcus aureusProtein sequencingProtein–protein interactionChemistryBioinformaticsBiologyPeptide sequenceBiochemistryGeneticsBacteriaGene

Abstract

fetched live from OpenAlex

Abstract Proteins essential for the viability of cells have been shown to have a higher probability of having a large number of interactions with other cell components. Thus it has been suggested that, by identifying the most connected proteins, hubs, in Protein Interaction Networks (PINs), one may discover essential proteins that may be then isolated as prospective drug targets. Such an approach bears particular importance for the development of new drugs against emergent and drug‐resistant pathogens such as Methicillin‐Resistant Staphylococcus aureus 252 (MRSA).

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.281
Teacher spread0.239 · 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