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Record W2061378219 · doi:10.1109/iscas.2009.5118268

Location of exons in DNA sequences using digital filters

2009· article· en· W2061378219 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFractal and DNA sequence analysis
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNarrowbandBand-pass filterPassbandAlgorithmExonComputer scienceSignal processingFilter (signal processing)Digital signal processingPhysicsOpticsComputer visionBiologyTelecommunicationsGeneticsGene

Abstract

fetched live from OpenAlex

A filtering technique for the location of hot spots in proteins proposed recently is applied for the location of exons in DNA sequences. The technique involves conversion of a DNA character sequence into a numerical sequence using the electron-ion interaction potential values and then filtering the numerical sequence using a narrowband bandpass digital filter whose passband is centered at the period-3 frequency, i.e., 2π/3. The strength of the bandpass-filtered signal as a function of nucleotide location is then detected using a lowpass filter. A plot of the signal power versus location reveals the presence of exons as distinct peaks. Simulations have shown that the technique leads to more accurate exon locations than another computational technique based on the short-time discrete Fourier transform. Furthermore, the amount of computation required is reduced by as much as 97 percent thereby rendering the technique suitable for the processing of long DNA sequences, even complete genomes.

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 score0.007
Threshold uncertainty score0.163

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 head0.258
Teacher spread0.245 · 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

Citations15
Published2009
Admission routes2
Has abstractyes

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