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Record W2487883765 · doi:10.11234/gi1990.21.229

MODERN HOMOLOGY SEARCH

2008· article· en· W2487883765 on OpenAlex
Ming Li

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

VenueProceedings Genome Informatics Workshop/Genome informatics · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHeuristicsComputer scienceHomology (biology)SoftwareSensitivity (control systems)ZoomSpeedupTheoretical computer scienceParallel computingBiologyProgramming languageGeneticsEngineeringGeneOperating system

Abstract

fetched live from OpenAlex

Dynamic programming [1] has full sensitivity, but too slow for large scale homology search. FASTA / BLAST type of heuristics [2] trade sensitivity for speed. Can we have both sensitivity and speed? We present the mathematical theory of optimized spaced seeds which allows modern homology search to achieve high sensitivity and high speed simultaneously. The spaced seed methodology is implemented in our PatternHunter software [3, 4], as well as many other modern homology search software, serving thousands of queries daily. The theory is then extended and implemented in ZOOM [5] to do fast genome scale reads mapping for the second generation sequencers.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.021
GPT teacher head0.235
Teacher spread0.214 · 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