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Record W2117420531 · doi:10.1002/anie.200902577

Direct, Electronic MicroRNA Detection for the Rapid Determination of Differential Expression Profiles

2009· article· en· W2117420531 on OpenAlexaff
Hong Yang, Leyla Soleymani, Fei‐Fei Liu, Edward H. Sargent, Shana O. Kelley

Bibliographic record

VenueAngewandte Chemie International Edition · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity Health NetworkOntario Institute for Cancer ResearchUniversity of Toronto
Fundersnot available
KeywordsmicroRNAChemistryRNABiosensorMultiplexingChipNanotechnologyComputational biologyGeneBiochemistryMaterials scienceBiologyComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

In the time it takes for lunch: An electronic chip featuring nanostructured microelectrodes (NMEs) enables the analysis of microRNA expression profiles in just 30 min in small RNA samples without enzymatic amplification or sequence labeling. The multiplexed chip detects the hybridization of microRNA targets to NME surfaces and provides large electrocatalytic gain through the use of an ultrasensitive redox reporter system (see picture).

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.007
GPT teacher head0.265
Teacher spread0.257 · 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 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

Citations145
Published2009
Admission routes1
Has abstractyes

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