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Record W2147140531 · doi:10.1109/icccas.2008.4657895

Wavelet analysis of DNA sequences

2008· article· en· W2147140531 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

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFractal and DNA sequence analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsWaveletWavelet transformCoding regionCaenorhabditis elegansDiscrete wavelet transformDNA sequencingBiologyCoding (social sciences)Drosophila melanogasterArtificial intelligenceWavelet packet decompositionComputational biologyPattern recognition (psychology)Computer scienceDNAGeneticsGeneMathematicsStatistics

Abstract

fetched live from OpenAlex

In this paper, the discrete wavelet transform, the complex wavelet transform, and the wavelet packet transform are applied to denoise G and C binary indicator-type of DNA sequences derived from both the Voss representation and the Z curve representation. The technique aims to reveal coding and non-coding regions of genes in a DNA sequence. DNA sequences of four selected model organisms including a model plant: Arabidopsis thaliana; a model vertebrate: Rattus norvegicus (a rat); two model invertebrates: Caenorhabditis elegans (a worm), and Drosophila melanogaster (a fruit fly) are used in the simulations to verify the effectiveness of the technique.

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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.264

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 head0.014
GPT teacher head0.243
Teacher spread0.229 · 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

Quick stats

Citations6
Published2008
Admission routes1
Has abstractyes

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