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Record W2020879581 · doi:10.1109/cibcb.2010.5510703

Classifying Cytochrome c Oxidase subunit 1 by translation initiation mechanism using side effect machines

2010· article· en· W2020879581 on OpenAlexafffund
Justin Schonfeld, Dan Ashlock

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaOntario Genomics InstituteGenome Canada
KeywordsMechanism (biology)Cytochrome c oxidaseTranslation (biology)Side effect (computer science)Protein subunitComputer scienceMachine translationSIGNAL (programming language)Computational biologyGeneArtificial intelligenceOxidase testBiologyEnzymeGeneticsMitochondrionBiochemistryMessenger RNAPhysics

Abstract

fetched live from OpenAlex

Cytochrome c oxidase subunit 1 (cox1) is unusual among mitochondrial genes in that instead of using AUG or one of the recognized alternative start codons it often appears to use an unknown means for initiating translation. However, the frequency of this unusual behavior as well as the underlying molecular mechanism are unknown. In this paper we use side effect machines to probe for signal in the sequence. Evolved side effect machines were able to correctly classify cox1 genes with ambiguous start codons 80.1% of the time. Side effect machines are finite state machines that have side effects associated with their states. In this study a simple side effect, a counter for the number of times the state was entered, is used. The problem is found to be challenging, a substantial majority of replicates found no signal, but some classifiers with statistically significant classification ability were located.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.275
Teacher spread0.264 · 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 designSimulation or modeling
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

Citations8
Published2010
Admission routes2
Has abstractyes

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Same topicMachine Learning in BioinformaticsFrench-language works237,207