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Record W2054453573 · doi:10.1089/cmb.2007.0208

New, Improved, and Practical k-Stem Sequence Similarity Measures for Probe Design

2008· article· en· W2054453573 on OpenAlexfundno aff
Anthony J. Macula, Alexander Schliep, Morgan Bishop, Thomas E. Renz

Bibliographic record

VenueJournal of Computational Biology · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Chromatin Dynamics
Canadian institutionsnot available
FundersUniversity of British Columbia
KeywordsNearest neighbor searchSimilarity (geometry)k-nearest neighbors algorithmSequence (biology)Computer scienceOligomer restrictionData miningBit arrayAlgorithmPattern recognition (psychology)OligonucleotideMathematicsArtificial intelligenceBiologyGeneticsGene

Abstract

fetched live from OpenAlex

We define new measures of sequence similarity for oligonucleotide probe design. These new measures incorporate the nearest neighbor k-stem motifs in their definition, but can be efficiently computed by means of a bit-vector method. They are not as computationally costly as algorithms that predict nearest neighbor hybridization potential. Our new measures for sequence similarity correlate significantly better with nearest neighbor thermodynamic predictions than either BLAST or the standard edit or insertion-deletion defined similarities already in use in many different probe design applications.

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 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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.805
Threshold uncertainty score0.296

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 head0.049
GPT teacher head0.305
Teacher spread0.256 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations6
Published2008
Admission routes1
Has abstractyes

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