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Record W2087128764 · doi:10.3389/fgene.2011.00098

Interpreting Gene-Expression Profiles in Transplantation: A Critical Appraisal

2011· article· en· W2087128764 on OpenAlexaff
Nicolas Pallet, Éric Thervet, Michel Desjardins

Bibliographic record

VenueFrontiers in Genetics · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCritical appraisalTransplantationGene expressionBiologyComputational biologyExpression (computer science)GeneGene expression profilingGeneticsComputer scienceMedicinePathologyInternal medicine

Abstract

fetched live from OpenAlex

Oligonucleotide microarray technology has created a small revolution in the transplant community because it has helped to decipher previously unknown molecular processes involved in allograft pathology, redefined molecular patterns of diseases that are indistinguishable at the pathological level and made possible the definition of new prognostic factors for long-term graft outcomes. However, given the tremendous complexity of the biological processes that are involved in the pathology of a transplanted organ, the interpretation of transcriptomic data can be speculative and oversimplified. Here, we discuss critical considerations regarding the nature of the object studied by cDNA microarray technology, the means by which it is observed, the interpretation of the observations, and whether the observations make sense in the context of transplant-related scientific questions. Given these limitations, we believe that global approaches based on more functional biological intermediates are necessary for a better understanding of the molecular processes that regulate the physiopathology of the transplanted organ.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.563

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 head0.012
GPT teacher head0.267
Teacher spread0.256 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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