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Record W4390669187 · doi:10.1038/s42003-023-05671-8

A revised nomenclature for the lemur family of protein kinases

2024· review· en· W4390669187 on OpenAlexaff
Gábor M. Mórotz, Neil A. Bradbury, Oana Caluseriu, Shin‐ichi Hisanaga, Christopher C.J. Miller, Agnieszka Swiatecka‐Urban, Heinz‐Josef Lenz, Stephen J. Moss, Georgios Giamas

Bibliographic record

VenueCommunications Biology · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital Edmonton
FundersNational Heart, Lung, and Blood InstituteNational Institute of Diabetes and Digestive and Kidney DiseasesNemzeti Kutatási Fejlesztési és Innovációs HivatalBiotechnology and Biological Sciences Research CouncilAlzheimer’s SocietyMedical Research CouncilNemzeti Kutatási, Fejlesztési és Innovaciós AlapHungarian Scientific Research FundMagyar Tudományos AkadémiaAlzheimer’s Research UKAlzheimer's SocietyNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsLemurKinaseBiologyComputational biologyNomenclatureProtein-Serine-Threonine KinasesNeuroscienceBioinformaticsProtein kinase ACell biologyTaxonomy (biology)ZoologyPrimate

Abstract

fetched live from OpenAlex

The lemur family of protein kinases has gained much interest in recent years as they are involved in a variety of cellular processes including regulation of axonal transport and endosomal trafficking, modulation of synaptic functions, memory and learning, and they are centrally placed in several intracellular signalling pathways. Numerous studies have also implicated role of the lemur kinases in the development and progression of a wide range of cancers, cystic fibrosis, and neurodegenerative diseases. However, parallel discoveries and inaccurate prediction of their kinase activity have resulted in a confusing and misleading nomenclature of these proteins. Herein, a group of international scientists with expertise in lemur family of protein kinases set forth a novel nomenclature to rectify this problem and ultimately help the scientific community by providing consistent information about these molecules.

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)0.003
metaresearch head score (Gemma)0.003
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: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0050.004
Science and technology studies0.0010.003
Scholarly communication0.0020.006
Open science0.0030.002
Research integrity0.0020.009
Insufficient payload (model declined to judge)0.0040.007

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 head0.087
GPT teacher head0.426
Teacher spread0.338 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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