GenoCanyon
Scores a position by the posterior probability that it is functional, from unsupervised statistical learning over conservation and biochemical annotation.
Results are recorded on linked configurations and versions of this method. Their settings and evaluations are kept separate below.
Results on linked records
This method has no results of its own in this release. 4 linked records have results. They are not assigned to the underlying method: each record keeps its own settings and evaluations, and its scores are on its own page.
- GenoCanyon (Cubuk et al. 2021) Configuration · configuration · 2 evaluations · 6 results
- GenoCanyon (Brandes et al. 2023 clinical benchmarks) Configuration · configuration · 4 evaluations · 8 results
- GenoCanyon (Chen et al. 2020) Configuration · configuration · 2 evaluations · 10 results
- GenoCanyon (Livesey and Marsh 2025) Configuration · configuration · 6 evaluations · 7 results
Overview
An explanatory profile has not yet been reviewed for this record.
Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.
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How it works, versions and access
Strengths, limitations and unresolved questions
Evidence
Source checking verifies the cited claim or transcription. It does not establish independent reproduction.
Evidence table
Inspect claims, sources and review details
Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
1 evidence row matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Description Scores a position by the posterior probability that it is functional, from unsupervised statistical learning over conservation and biochemical annotation. Individual claims | Comprehensive assessment of computational algorithms in predicting cancer driver mutations somatic-oncogenicity-20261009-source-chen2020: Table 1, Summary of 33 computational algorithms included in this study Version: Genome Biology 21:43, published 2020-02-20; PMC7033911 full-text XML | source checked ["automated-source-review","ai-assisted-source-review"] · 2026-10-11 Audit detailsApproved with two corrections, both applied: the MutFormer source licence is CC BY 4.0 per the arXiv abstract page, and the MetaRNN source locator now names the Background passage. Checked the 27 Chen 2020 descriptions row by row against Table 1 (PMC7033911) and spot-checked AlphaMissense, MisFit, popEVE, VARITY, MetaRNN, MutFormer and gMVP against their abstracts; accurate. Licences on the other new sources match Europe PMC. Field: Claim: metadata-correction-14eacb679e92c069533b Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
Release 2026-10-22-1b7cf027e4f6 · Record review: source checked
1 source record and release history
- Comprehensive assessment of computational algorithms in predicting cancer driver mutations · Original source · Genome Biology 21:43, published 2020-02-20; PMC7033911 full-text XML
Technical metadata and extraction receipts
Stable ID: somatic-oncogenicity-20261009-method-genocanyon
- areas
- dna-genomes
- contexts
- clinical_research
- method types
- specialist
- reported name
- GenoCanyon
- entity level
- method
- source locator
- Algorithm lists of the cited sources
- missing metadata
- version: reason: inapplicable; note: Family record; score sources are on configurations
Related records
- configuration of: GenoCanyon (Cubuk et al. 2021)
- configuration of: GenoCanyon (Brandes et al. 2023 clinical benchmarks)
- subject: GenoCanyon: description
- configuration of: GenoCanyon (Chen et al. 2020)
- configuration of: GenoCanyon (Livesey and Marsh 2025)