MetaRNN
Recurrent neural network over other predictors' scores, conservation scores and population allele frequencies, with flanking-codon annotations read by bidirectional gated recurrent units, trained to separate rare pathogenic from rare benign nonsynonymous variants; a companion model covers non-frameshift indels.
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.
- MetaRNN (Brandes et al. 2023 head-to-head comparison) Configuration · configuration · 2 evaluations · 4 results
- MetaRNN rank score, dbNSFP 5.3.1a (Lee 2026) Configuration · configuration · 3 evaluations · 6 results
- MetaRNN (Livesey and Marsh 2025) Configuration · configuration · 6 evaluations · 7 results
- MetaRNN [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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How it works, versions and access
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 Recurrent neural network over other predictors' scores, conservation scores and population allele frequencies, with flanking-codon annotations read by bidirectional gated recurrent units, trained to separate rare pathogenic from rare benign nonsynonymous variants; a companion model covers non-frameshift indels. Individual claims | MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning qa-descriptions-20261011-e-source-metarnn: Abstract; Background (flanking ±1 codon annotations read by bidirectional GRUs) Version: Europe PMC full-text XML for PMC9548151, Genome Medicine 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-ea8831aa2d7ea86ef6a3 Source artifact SHA-256: Hash scope: SHA-256 of one retrieval of the Europe PMC full-text XML. These renderings are not always byte-stable; the article text is 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
- MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning · Original source · Europe PMC full-text XML for PMC9548151, Genome Medicine 2022
Technical metadata and extraction receipts
Stable ID: somatic-oncogenicity-20261009-method-metarnn
- areas
- dna-genomes
- contexts
- clinical_research
- method types
- supervised_machine_learning
- reported name
- MetaRNN
- 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: MetaRNN (Brandes et al. 2023 head-to-head comparison)
- subject: MetaRNN: description
- configuration of: MetaRNN rank score, dbNSFP 5.3.1a (Lee 2026)
- configuration of: MetaRNN (Livesey and Marsh 2025)
- configuration of: MetaRNN [ProteinGym 144fe22 clinical summary]