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Record W2169160107 · doi:10.1109/tnb.2005.853665

DNA Microarray Stochastic Model

2005· article· en· W2169160107 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on NanoBioscience · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene expression and cancer classification
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPixelNoise (video)Gaussian noiseComputer scienceGaussian processIntensity (physics)GaussianPattern recognition (psychology)AlgorithmArtificial intelligenceBiological systemImage (mathematics)PhysicsOpticsBiology

Abstract

fetched live from OpenAlex

A stochastic model of the DNA microarray image pixels is presented. The model includes spot pixel intensity distribution, interpixel correlations and the intensity distribution of background noise. The data is indicative of a small exponential additive noise process and a larger Gaussian fluctuation that scales with spot intensity. Correlations are observed among pixels in the spot and between test and control images. The correlated fluctuations may be attributed to variations across each spot in the amount of DNA placed on the spot during the array fabrication process. The model may be used in gene expression estimation algorithm development, both to test new algorithms through simulation and to develop optimum algorithms. The model should also be easily adapted to new array based technologies in proteomics.

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.

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: none
Teacher disagreement score0.857
Threshold uncertainty score0.423

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 head0.015
GPT teacher head0.254
Teacher spread0.240 · 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