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Record W4401914768 · doi:10.1186/s12859-024-05885-5

HerpesDRG: a comprehensive resource for human herpesvirus antiviral drug resistance genotyping

2024· article· en· W4401914768 on OpenAlexaff
Oscar Charles, Cristina Venturini, Richard A. Goldstein, Judith Breuer

Bibliographic record

VenueBMC Bioinformatics · 2024
Typearticle
Languageen
FieldMedicine
TopicHerpesvirus Infections and Treatments
Canadian institutionsInstitute of Infection and Immunity
FundersUniversity College London Hospitals Biomedical Research CentreMedical Research CouncilUniversity College London Hospitals NHS Foundation TrustNational Institute for Health and Care ResearchUK Research and InnovationWellcome Trust
KeywordsDrug resistanceGenotypingSanger sequencingBiologyHuman herpesvirus 6AnnotationMutationComputational biologyGenotypeBioinformaticsDatabaseGeneticsGeneVirusComputer scienceViral diseaseHerpesviridae

Abstract

fetched live from OpenAlex

The prevention and treatment of many herpesvirus associated diseases is based on the utilization of antiviral therapies, however therapeutic success is limited by the development of drug resistance. Currently no single database cataloguing resistance mutations exists, which hampers the use of sequence data for patient management. We therefore developed HerpesDRG, a drug resistance mutation database that incorporates all the known resistance genes and current treatment options, built from a systematic review of available genotype to phenotype literature. The database is released along with an R package that provides a simple approach to resistance variant annotation and clinical implication analysis from common sanger and next generation sequencing data. This represents the first openly available and community maintainable database of drug resistance mutations for the human herpesviruses (HHV), developed for the community of researchers and clinicians tackling HHV drug resistance.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.866

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 head0.036
GPT teacher head0.321
Teacher spread0.285 · 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 designNot applicable
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

Citations4
Published2024
Admission routes1
Has abstractyes

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