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Record W2121621283 · doi:10.1093/bioinformatics/btg004

IslandPath: aiding detection of genomic islands in prokaryotes

2003· article· en· W2121621283 on OpenAlexaff
William Hsiao, Ivan Wan, Steven J.M. Jones, Fiona S. L. Brinkman

Bibliographic record

VenueBioinformatics · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsBC Cancer AgencySimon Fraser University
Fundersnot available
KeywordsComputational biologyComputer scienceBiology

Abstract

fetched live from OpenAlex

UNLABELLED: Genomic islands (clusters of genes of potential horizontal origin in a prokaryotic genome) are frequently associated with a particular adaptation of a microbe that is of medical, agricultural or environmental importance, such as antibiotic resistance, pathogen virulence, or metal resistance. While many sequence features associated with such islands have been adopted separately in applications for analysis of genomic islands, including pathogenicity islands, there is no single application that integrates multiple features for island detection. IslandPath is a network service which incorporates multiple DNA signals and genome annotation features into a graphical display of a bacterial or archaeal genome, to aid the detection of genomic islands. AVAILABILITY: This application is available at http://www.pathogenomics.sfu.ca/islandpath and the source code is freely available, under GNU public licence, from the authors. SUPPLEMENTARY INFORMATION: An online help file, which includes analyses of the utility of IslandPath, can be found at http://www.pathogenomics.sfu.ca/islandpath/current/islandhelp.html

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)0.001
metaresearch head score (Gemma)0.005
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: Software · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.007

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 head0.023
GPT teacher head0.216
Teacher spread0.193 · 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
GenreSoftware

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

Citations395
Published2003
Admission routes1
Has abstractyes

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