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Record W2019454936 · doi:10.1016/s0014-5793(01)03295-1

Normalization of nomenclature for peptide motifs as ligands of modular protein domains

2002· review· en· W2019454936 on OpenAlexfundno aff
Rein Aasland, Charles S. Abrams, Christophe Ampè, Linda Ball, Mark T. Bedford, Gianni Cesareni, Mario Gimona, James H. Hurley, Thomas Jarchau, Veli-Pekka Lehto, Mark A. Lemmon, Rune Linding, Bruce J. Mayer, Makoto Nagai, Marius Sudol, Ulrich Walter, Steve J. Winder

Bibliographic record

VenueFEBS Letters · 2002
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsnot available
FundersNational Institutes of HealthInternational Union of Biochemistry and Molecular BiologyInternational Union of Pure and Applied Chemistry
KeywordsNormalization (sociology)NomenclatureComputer scienceModular designParsingComputational biologyBiologyArtificial intelligenceProgramming languageTaxonomy (biology)

Abstract

fetched live from OpenAlex

We propose a normalization of symbols and terms used to describe, accurately and succinctly, the detailed interactions between amino acid residues of pairs of interacting proteins at protein:protein (or protein:peptide) interfaces. Our aim is to unify several diverse descriptions currently in use in order to facilitate communication in the rapidly progressing field of signaling by protein domains. In order for the nomenclature to be convenient and widely used, we also suggest a parallel set of symbols restricted to the ASCII format allowing accurate parsing of the nomenclature to a computer-readable form. This proposal will be reviewed in the future and will therefore be open for the inclusion of new rules, modifications and changes.

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)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0010.004
Scholarly communication0.0020.004
Open science0.0030.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.004

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 head0.024
GPT teacher head0.308
Teacher spread0.284 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations134
Published2002
Admission routes1
Has abstractyes

Explore more

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