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Record W2996887063 · doi:10.1093/bioinformatics/btz918

MLDSP-GUI: an alignment-free standalone tool with an interactive graphical user interface for DNA sequence comparison and analysis

2019· article· en· W2996887063 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBioinformatics · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFractal and DNA sequence analysis
Canadian institutionsUniversity of WaterlooWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGraphical user interfaceExecutableComputer scienceGraphical user interface testingSoftwareUser interfaceOpen sourceSource codeData miningProgramming languagePython (programming language)User interface design

Abstract

fetched live from OpenAlex

SUMMARY: Machine Learning with Digital Signal Processing and Graphical User Interface (MLDSP-GUI) is an open-source, alignment-free, ultrafast, computationally lightweight, and standalone software tool with an interactive GUI for comparison and analysis of DNA sequences. MLDSP-GUI is a general-purpose tool that can be used for a variety of applications such as taxonomic classification, disease classification, virus subtype classification, evolutionary analyses, among others. AVAILABILITY AND IMPLEMENTATION: MLDSP-GUI is open-source, cross-platform compatible, and is available under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/). The executable and dataset files are available at https://sourceforge.net/projects/mldsp-gui/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.662

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 head0.013
GPT teacher head0.284
Teacher spread0.270 · 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