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Record W2092942228 · doi:10.1186/1756-0500-3-264

Using gene expression profiles from peripheral blood to identify asymptomatic responses to acute respiratory viral infections

2010· article· en· W2092942228 on OpenAlexfundno aff
Alexander Statnikov, Nikita I. Lytkin, Lauren McVoy, Jörn-Hendrik Weitkamp, Constantin Aliferis

Bibliographic record

VenueBMC Research Notes · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene expression and cancer classification
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Center for Research ResourcesU.S. National Library of MedicineNational Institute of Child Health and Human DevelopmentNational Institutes of HealthSchool of Medicine, New York UniversityYork University
KeywordsAsymptomaticImmune systemImmunologyMicroarrayViral loadMicroarray analysis techniquesRespiratory systemGene expression profilingMedicineGene chip analysisPeripheral bloodGene expressionTranscriptomeVirologyBiologyGeneVirusInternal medicineGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: A recent study reported that gene expression profiles from peripheral blood samples of healthy subjects prior to viral inoculation were indistinguishable from profiles of subjects who received viral challenge but remained asymptomatic and uninfected. If true, this implies that the host immune response does not have a molecular signature. Given the high sensitivity of microarray technology, we were intrigued by this result and hypothesize that it was an artifact of data analysis. FINDINGS: Using acute respiratory viral challenge microarray data, we developed a molecular signature that for the first time allowed for an accurate differentiation between uninfected subjects prior to viral inoculation and subjects who remained asymptomatic after the viral challenge. CONCLUSIONS: Our findings suggest that molecular signatures can be used to characterize immune responses to viruses and may improve our understanding of susceptibility to viral infection with possible implications for vaccine development.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.029
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.119
GPT teacher head0.446
Teacher spread0.327 · 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

Citations9
Published2010
Admission routes1
Has abstractyes

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