MétaCan
Menu
Back to cohort
Record W2139181200 · doi:10.1155/s1110865704309285

A Digital Signal Processing Method for Gene Prediction with Improved Noise Suppression

2004· article· en· W2139181200 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

VenueEURASIP Journal on Advances in Signal Processing · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFractal and DNA sequence analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCoding (social sciences)Computer scienceDigital signal processingAlgorithmCoding regionGeneGene predictionSpeech recognitionPattern recognition (psychology)Artificial intelligenceMathematicsBiologyGeneticsGenomeStatistics

Abstract

fetched live from OpenAlex

It has been observed that the protein-coding regions of DNA sequences exhibit period-three behaviour, which can be exploited to predict the location of coding regions within genes. Previously, discrete Fourier transform (DFT) and digital filter-based methods have been used for the identification of coding regions. However, these methods do not significantly suppress the noncoding regions in the DNA spectrum at . Consequently, a noncoding region may inadvertently be identified as a coding region. This paper introduces a new technique (a single digital filter operation followed by a quadratic window operation) that suppresses nearly all of the noncoding regions. The proposed method therefore improves the likelihood of correctly identifying coding regions in such genes.

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 score0.843
Threshold uncertainty score0.868

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 head0.008
GPT teacher head0.286
Teacher spread0.277 · 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