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Record W3012499363 · doi:10.1101/cshperspect.a038596

Systems Biological Analysis of Immune Response to Influenza Vaccination

2020· article· en· W3012499363 on OpenAlexaff
Mario Cortese, Amy C Sherman, Nadine Rouphael, Bali Pulendran

Bibliographic record

VenueCold Spring Harbor Perspectives in Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsInstitute of Infection and Immunity
FundersNational Institute of Allergy and Infectious DiseasesNational Institute on AgingNational Institutes of HealthEmory University
KeywordsVaccinationImmunogenicityReverse vaccinologyImmunityImmune systemInfluenza vaccineComputational biologyBiologyImmunologyVirologyAntigen

Abstract

fetched live from OpenAlex

The last decade has witnessed tremendous progress in immunology and vaccinology, owing to several scientific and technological breakthroughs. Systems vaccinology is a field that has emerged at the forefront of vaccine research and development and provides a unique way to probe immune responses to vaccination in humans. The goals of systems vaccinology are to use systems-based approaches to define signatures that can be used to predict vaccine immunogenicity and efficacy and to delineate the molecular mechanisms driving protective immunity. The application of systems biological approaches in influenza vaccination studies has enabled the discovery of early signatures that predict immunogenicity to vaccination and yielded novel mechanistic insights about vaccine-induced immunity. Here we review the contributions of systems vaccinology to influenza vaccine development and critically examine the potential of systems vaccinology toward enabling the development of a universal influenza vaccine that provides robust and durable immunity against diverse influenza viruses.

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)0.002
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
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 head0.122
GPT teacher head0.405
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.

Study designObservational
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

Citations19
Published2020
Admission routes1
Has abstractyes

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