MétaCan
Menu
Back to cohort
Record W3118307481 · doi:10.1073/pnas.2025207118

Standardizing gene product nomenclature—a call to action

2021· article· en· W3118307481 on OpenAlexaff
Kenji Fujiyoshi, Elspeth A. Bruford, Paweł Mróz, Cynthe Sims, Timothy J. O’Leary, Anthony W.I. Lo, Neng Chen, Nimesh R. Patel, Keyur P. Patel, Barbara Seliger, Mingyang Song, Federico A. Monzon, Alexis B. Carter, Margaret L. Gulley, Susan M. Mockus, Thuy L. Phung, Harriet Feilotter, Heather Williams, Shuji Ogino

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsKingston Health Sciences CentreQueen's University
FundersNational Cancer InstituteNational Human Genome Research InstituteWellcome Trust
KeywordsNomenclatureGene nomenclatureComputational biologyAction (physics)BiologyGeneticsZoologyTaxonomy (biology)

Abstract

fetched live from OpenAlex

The current lack of a standardized nomenclature system for gene products (e.g., proteins) has resulted in a haphazard counterproductive system of labeling. Different names are often used for the same gene product; the same name is sometimes used for unrelated gene products. Such ambiguity causes not only potential harm to patients, whose treatments increasingly rely on laboratory tests for multiple gene products, but also miscommunication and inefficiency, both of which hinder progress of broad scientific fields. To mitigate this confusion, we recommend standardizing human protein nomenclature through the use of a Human Genome Organisation (HUGO) Gene Nomenclature Committee (HGNC) gene symbol accompanied by its unique HGNC ID. We call for action across all biomedical communities and scientific and medical journals to standardize nomenclature of gene products using HGNC gene symbols to enhance accuracy in scientific and public communication. We call on all biomedical communities and scientific and medical journals to standardize nomenclature of gene products to enhance accuracy in scientific and public communication. Image credit: Shutterstock/greenbutterfly. Use of gene symbols designated by the HGNC [www.genenames.org (1)] is nearly universal. DNA- and RNA-level sequence variation nomenclature has been standardized to use HGNC gene symbols, the Single Nucleotide Polymorphism database (dbSNP) IDs, and genetic variant nomenclature designated by the Human Genome Variation Society (HGVS) to unambiguously designate variants. In striking contrast to the use of universal identifiers for genes and gene variants, there are no universal identifiers for the peptides and proteins that these genes encode. Many gene products have multiple nomenclatures in widespread use, and many common nomenclatures are used for multiple gene products. For example, the symbol “PD-1” is shared by multiple unrelated gene products and is used to describe PDCD1 , SNCA , and SPATA2 gene products. The PDCD1 (PD-1) protein is a well-known target for cancer immunotherapy. … [↵][1]1To whom correspondence may be addressed. Email: elspeth@ebi.ac.uk, timothy.oleary@va.gov, or sogino{at}bwh.harvard.edu. [1]: #xref-corresp-1-1

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 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 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 score0.021
Threshold uncertainty score0.150

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.033
GPT teacher head0.315
Teacher spread0.282 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations53
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicGenomics and Rare DiseasesFrench-language works237,207