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Record W4389679091 · doi:10.1101/2023.12.09.570948

Variant Mapping Application: Customize And Annotate Figures Of Voltage-Gated Sodium Channel

2023· preprint· en· W4389679091 on OpenAlexaff
Winnie Wen, Arjun Mahadevan, R. Ryley Parrish, Richard A. Dean, Alison Cutts, J. P. Johnson

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon channel regulation and function
Canadian institutionsXenon Pharmaceuticals (Canada)University of British Columbia
Fundersnot available
KeywordsComputer scienceIon channelSodium channelComputational biologyChannel (broadcasting)PhenotypeMutationGeneBiologyGeneticsChemistrySodium

Abstract

fetched live from OpenAlex

Abstract Voltage-gated sodium ion channels allow for the initiation and transmission of action potentials. There is a high interest in research and drug development to selectively target these ion channels to treat epilepsy and other disorders such as pain. Scientific literature and presentations often incorporate maps of these integral membrane proteins with markers indicating gene mutations to highlight genotype/phenotype correlations. There is a need for automated tools to create high quality figures with mutation (variant) locations displayed on these channel maps. This manuscript introduces a simple application to create visualization for mutations on alpha voltage-gated sodium channels, created using the D3.js library. The application allows for mapping of variant sequences, as well as important properties like the type of variant and the phenotypes linked to the variant. It also allows for customizability and the production of high-quality images for publication. This application and code base can further be extrapolated to other ion channels as well.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Software · Consensus signal: Software
Teacher disagreement score0.103
Threshold uncertainty score0.343

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1030.048

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 head0.016
GPT teacher head0.219
Teacher spread0.202 · 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 designSimulation or modeling
Domainnot available
GenreSoftware

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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicIon channel regulation and function→French-language works237,207→