gMVP
Graph attention neural network whose nodes carry amino-acid features and whose edges are weighted by co-evolution strength, pooling information from the local protein context and from correlated distal positions.
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. 3 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.
- gMVP rank score, dbNSFP 5.3.1a (Lee 2026) Configuration · configuration · 3 evaluations · 6 results
- gMVP (Livesey and Marsh 2025) Configuration · configuration · 6 evaluations · 7 results
- gMVP [ProteinGym 144fe22 clinical summary] Configuration · configuration · 1 evaluation · 1 result
Overview
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Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.
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Strengths, limitations and unresolved questions
Evidence
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Evidence table
Inspect claims, sources and review details
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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 Graph attention neural network whose nodes carry amino-acid features and whose edges are weighted by co-evolution strength, pooling information from the local protein context and from correlated distal positions. Individual claims | Abstract: Predicting functional effect of missense variants using graph attention neural networks (gMVP) qa-descriptions-20261011-e-source-gmvp: abstractText Version: Europe PMC core record MED/37484202 (title and abstract), Nature Machine Intelligence 2022 | 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-5bf3b695023c49ff6f70 Source artifact SHA-256: Hash scope: SHA-256 of one retrieval of a Europe PMC REST search response. These responses are not byte-stable (a later retrieval can differ in JSON key order); the title and abstractText fields are the evidence. |
Sources and history
Release 2026-10-22-1b7cf027e4f6 · Record review: source checked
2 source records and release history
- An openly licensed benchmark and per-gene calibration map for missense pathogenicity predictors on activating cancer drivers · Original source · bioRxiv 2026.07.16.739080 v1, posted 2026-07-23; full-text HTML page
- Abstract: Predicting functional effect of missense variants using graph attention neural networks (gMVP) · Original source · Europe PMC core record MED/37484202 (title and abstract), Nature Machine Intelligence 2022
Technical metadata and extraction receipts
Stable ID: somatic-oncogenicity-20261009-method-gmvp
- areas
- dna-genomes
- contexts
- clinical_research
- method types
- supervised_machine_learning
- reported name
- gMVP
- 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
- subject: gMVP: description
- configuration of: gMVP rank score, dbNSFP 5.3.1a (Lee 2026)
- configuration of: gMVP (Livesey and Marsh 2025)
- configuration of: gMVP [ProteinGym 144fe22 clinical summary]