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Record W4412626392 · doi:10.1093/bioadv/vbaf178

Gene-set enrichment analysis and visualization on the web using EnrichmentMap:RNASeq

2024· article· en· W4412626392 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.

Bibliographic record

VenueBioinformatics Advances · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioinformatics and Genomic Networks
Canadian institutionsLunenfeld-Tanenbaum Research InstitutePrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
FundersNational Institutes of Health
KeywordsVisualizationComputational biologySet (abstract data type)BiologyComputer scienceGeneticsWorld Wide WebData miningProgramming language

Abstract

fetched live from OpenAlex

Summary: EnrichmentMap: RNASeq (enrichmentmap.org) is an intuitive, web-based app for gene-set enrichment analysis and visualization, specifically supporting two-case RNA-Seq experiments for Homo sapiens. The web app introduces a simplified user interface, faster processing times, and eliminates the need for software installation compared to running similar workflows in the Cytoscape desktop software, catering to biologists with minimal computational experience. EnrichmentMap: RNASeq is a new type of Cytoscape web app that is interoperable with Cytoscape. Availability and implementation: The app is available to use at enrichmentmap.org and the source code is available at github.com/cytoscape/enrichment-map-webapp.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score0.510

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 head0.279
Teacher spread0.266 · 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