MetaRNN: description
Description of 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.
Replace a category-only description with a sourced functional description (issue #88). Historical releases retain the original value.
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.
7 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Claimed field description Context-only references | 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 | not individually reviewed No individual claim review recorded Audit detailsField: 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. |
| Previous value Variant effect predictor for missense variants. Context-only references | 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 | not individually reviewed No individual claim review recorded Audit detailsField: 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. |
| Source location qa-descriptions-20261011-e-source-metarnn: Abstract; Background (flanking ±1 codon annotations read by bidirectional GRUs) Context-only references | 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 | not individually reviewed No individual claim review recorded Audit detailsField: 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. |
| Claimed value 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. Context-only references | 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 | not individually reviewed No individual claim review recorded Audit detailsField: 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. |
| Description Replace a category-only description with a sourced functional description (issue #88). Historical releases retain the original value. Context-only references | 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 | not individually reviewed No individual claim review recorded Audit detailsField: 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. |
| Relationship: subject somatic-oncogenicity-20261009-method-metarnn Context-only references | 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 | not individually reviewed No individual claim review recorded Audit detailsField: 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. |
| Name MetaRNN: description Context-only references | 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 | not individually reviewed No individual claim review recorded Audit detailsField: 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
1 source record and release history
- 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: metadata-correction-ea8831aa2d7ea86ef6a3
- areas
- dna-genomes
- contexts
- clinical_research
- method types
- supervised_machine_learning
- field
- description
- previous value
- Variant effect predictor for missense variants.
- value
- 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.
- source locator
- qa-descriptions-20261011-e-source-metarnn: Abstract; Background (flanking ±1 codon annotations read by bidirectional GRUs)
- review
- method: automated-source-review; ai-assisted-source-review; reviewer: claude; reviewer note: Extractor: Claude integrator agent for issue #88. Reviewer: a separate Claude review agent, independent of the extractor; no human review claimed.; date: 2026-10-11; method note: Compared the store diff with origin/main, read each new description against its cited source passage and checked the licence of each new source record.; note: Approved 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.; receipt: data/omics/qa-descriptions-20261011-e/review.json
Related records
- subject: MetaRNN