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Record W2108102223 · doi:10.1093/bioinformatics/bts695

ChromoZoom: a flexible, fluid, web-based genome browser

2012· article· en· W2108102223 on OpenAlexaff
Theodore R. Pak, Frederick P. Roth

Bibliographic record

VenueBioinformatics · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
FundersNational Human Genome Research InstituteNational Heart, Lung, and Blood InstituteUniversity of California, Santa Cruz
KeywordsComputer scienceScrollingZoomRendering (computer graphics)Source codeWeb serverGenome browserUser interfaceServerWeb applicationComputer graphics (images)Operating systemWorld Wide WebThe InternetGenomeGenomics

Abstract

fetched live from OpenAlex

UNLABELLED: Current web-based genome browsers require repetitious user input to scroll over long distances, alter the drawing density of elements or zoom through multiple orders of magnitude. Generally, either the server or the client is responsible for the majority of data processing, resulting in either servers having to receive and handle data relevant only to one user, or clients redundantly processing widely viewed data. ChromoZoom pre-renders and caches general-use tracks into tiled images on the server and serves them in an interactive web interface with inertial scrolling and precise, fluent zooming via the mouse wheel or trackpad. Custom tracks in several formats can be rendered by client-side code alongside the pre-rendered tracks, minimizing server load because of user-specific rendering and eliminating the need to transmit private data. ChromoZoom thereby enables rapid and simultaneous exploration of curated, experimental and personal genomic datasets. AVAILABILITY: Human and yeast genome researchers may browse recent assemblies within ChromoZoom at http://chromozoom.org/. Source code is available at http://github.com/rothlab/chromozoom/.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.703

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 head0.015
GPT teacher head0.232
Teacher spread0.217 · 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
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

Citations17
Published2012
Admission routes1
Has abstractyes

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