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Record W4388906989 · doi:10.1080/14789450.2023.2287054

How to maximize power for differential expression analysis in discovery omics through experimental design

2023· editorial· en· W4388906989 on OpenAlexafffund
Brenden Dufault, Richard D. LeDuc, René P. Zahedi

Bibliographic record

VenueExpert Review of Proteomics · 2023
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsResearch Institute in Oncology and HematologyChildren's Hospital Research Institute of ManitobaCancerCare ManitobaResearch ManitobaUniversity of ManitobaGeorge & Fay Yee Centre for Healthcare Innovation
FundersUniversity of Manitoba
KeywordsComputational biologyExpression (computer science)Differential (mechanical device)BiologyComputer scienceData miningBioinformaticsEngineering

Abstract

fetched live from OpenAlex

1. Unfortunately, ‘experimental design’ can mean different things to different researchers involved in the same discovery-omics study. For some, it can mean instrument configuration [1], experiment...

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)0.052
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.052
Threshold uncertainty score0.275

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.098
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0020.001
Science and technology studies0.0010.007
Scholarly communication0.0060.006
Open science0.0040.002
Research integrity0.0090.023
Insufficient payload (model declined to judge)0.0070.007

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 head0.014
GPT teacher head0.301
Teacher spread0.287 · 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 designTheoretical or conceptual
Domainnot available
GenreEditorial

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

Citations3
Published2023
Admission routes2
Has abstractyes

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