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Record W2134922741 · doi:10.1142/s0219720012710023

DETECTION AND DECOMPOSITION: TREATMENT-INDUCED CYCLIC GENE EXPRESSION DISRUPTION IN HIGH-THROUGHPUT TIME-SERIES DATASETS

2012· article· en· W2134922741 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

VenueJournal of Bioinformatics and Computational Biology · 2012
Typearticle
Languageen
FieldComputer Science
TopicEvolutionary Algorithms and Applications
Canadian institutionsLakehead University
Fundersnot available
KeywordsGeneGene expressionOrganismDecompositionComputational biologyBiologyExpression (computer science)Series (stratigraphy)Computer scienceGeneticsEcology

Abstract

fetched live from OpenAlex

Higher organisms possess many genes which cycle under normal conditions, to allow the organism to adapt to expected environmental conditions throughout the course of a day. However, treatment-induced disruption of regular cyclic gene expression patterns presents a significant challenge in novel gene discovery experiments because these disruptions can induce strong differential regulation events for genes that are not involved in an adaptive response to the treatment. To address this cycle disruption problem, we reviewed the state-of-art periodic pattern detection algorithms and a pattern decomposition algorithm (PRIISM), which is a knowledge-based Fourier analysis algorithm designed to distinguish the cyclic patterns from the rest gene expression patterns, and discussed potential future improvements.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.277

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 communication0.0000.001
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 head0.012
GPT teacher head0.277
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