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PaIntDB: network-based omics integration and visualization using protein–protein interactions in <i>Pseudomonas aeruginosa</i>

2021· article· en· W3162343445 on OpenAlexafffund
Javier J Castillo-Arnemann, Olga Solodova, Bhavjinder K. Dhillon, Robert E. W. Hancock

Bibliographic record

VenueBioinformatics · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioinformatics and Genomic Networks
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsInteractomeVisualizationComputer scienceSource codeComputational biologyPseudomonas aeruginosaWeb serverData miningBioinformaticsBiologyWorld Wide WebThe InternetGeneticsGeneProgramming language

Abstract

fetched live from OpenAlex

SUMMARY: The Pseudomonas aeruginosa Interaction Database, PaIntDB, is an intuitive web-based tool for network-based systems biology analyses using protein-protein interactions (PPI) in this medically important pathogen. It enables the integration and visualization of omics analyses including RNA-Seq and Tn-Seq. High-throughput datasets can be mapped onto PPI networks, which can be explored visually and filtered to uncover novel putative molecular pathways related to the conditions of study. PaIntDB contains the most comprehensive P.aeruginosa interactome to date, collected from a variety of resources, including interactions predicted computationally to further expand analysis capabilities. The web server implementation makes it easily extendable to other bacterial species. AVAILABILITY AND IMPLEMENTATION: PaIntDB is freely available at https://www.paintdb.ca, the source code and database file are available at https://github.com/yavyx/PaIntDB. 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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.808
Threshold uncertainty score0.822

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 head0.256
Teacher spread0.241 · 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 designSimulation or modeling
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

Citations3
Published2021
Admission routes2
Has abstractyes

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