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Record W2903973767 · doi:10.1002/cpbi.69

Using OmicsNet for Network Integration and 3D Visualization

2018· article· en· W2903973767 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

VenueCurrent Protocols in Bioinformatics · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioinformatics and Genomic Networks
Canadian institutionsMcGill UniversitySte. Anne's Hospital
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsGenome Canada
KeywordsVisualizationComputer scienceComputer graphics (images)Artificial intelligence

Abstract

fetched live from OpenAlex

OmicsNet is a novel web-based tool for creating and visualizing complex biological networks in 3D space. By coupling a comprehensive knowledgebase with the powerful WebGL technology, OmicsNet allows researchers to intuitively explore molecular interactions and regulatory relationships among genes, transcription factors, microRNAs, and metabolites. OmicsNet fills an important gap by facilitating multi-omics integration and systems biology. This article contains three basic protocols covering the key features of OmicsNet, including how to create biological networks from a single or multiple list(s) of molecules, how to integrate or enrich different types of networks, and how to navigate the 3D visualization system to obtain biological insights. The OmicsNet web server is freely available at https://www.omicsnet.ca. © 2018 by John Wiley & Sons, Inc.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.915
Threshold uncertainty score0.610

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 head0.054
GPT teacher head0.373
Teacher spread0.319 · 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