Dataset

Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)

CNVs with ACMG five-category classifications from five published clinical studies and an internal CNV-seq laboratory set, deduplicated and lifted to hg38.

Evaluation results

15 evaluations · 65 results. Different protocols are not a single leaderboard.

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	60.2% Accuracy (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'AnnotSV', column 'Accuracy (%)'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	38.1% F1 (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'AnnotSV', column 'F1(%)'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	11.2% False positive rate (ACMG five-category classification; averaging across classes not stated) percent · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'AnnotSV', column 'FPR*(%)'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	0.43 MCC (ACMG five-category classification; averaging across classes not stated) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'AnnotSV', column 'MCC*'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	40% Precision (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'AnnotSV', column 'PPV*(%)'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	43.5% Recall (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'AnnotSV', column 'Sensitivity (%)'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	88.8% Specificity (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source AnnotSV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'AnnotSV', column 'Specificity (%)'
Configuration: ClassifyCNV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	67.8% Accuracy (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ClassifyCNV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'ClassifyCNV', column 'Accuracy (%)'
Configuration: ClassifyCNV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	45.1% F1 (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ClassifyCNV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'ClassifyCNV', column 'F1(%)'
Configuration: ClassifyCNV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	10.3% False positive rate (ACMG five-category classification; averaging across classes not stated) percent · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ClassifyCNV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'ClassifyCNV', column 'FPR*(%)'
Configuration: ClassifyCNV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	0.54 MCC (ACMG five-category classification; averaging across classes not stated) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ClassifyCNV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'ClassifyCNV', column 'MCC*'
Configuration: ClassifyCNV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	52.3% Precision (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ClassifyCNV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'ClassifyCNV', column 'PPV*(%)'
Configuration: ClassifyCNV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	44% Recall (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ClassifyCNV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'ClassifyCNV', column 'Sensitivity (%)'
Configuration: ClassifyCNV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	89.7% Specificity (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ClassifyCNV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'ClassifyCNV', column 'Specificity (%)'
Configuration: dbCNV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	66.6% Accuracy (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source dbCNV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'dbCNV', column 'Accuracy (%)'
Configuration: dbCNV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	41% F1 (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source dbCNV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'dbCNV', column 'F1(%)'
Configuration: dbCNV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	9.4% False positive rate (ACMG five-category classification; averaging across classes not stated) percent · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source dbCNV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'dbCNV', column 'FPR*(%)'
Configuration: dbCNV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	0.51 MCC (ACMG five-category classification; averaging across classes not stated) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source dbCNV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'dbCNV', column 'MCC*'
Configuration: dbCNV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	40.9% Precision (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source dbCNV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'dbCNV', column 'PPV*(%)'
Configuration: dbCNV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	45.7% Recall (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source dbCNV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'dbCNV', column 'Sensitivity (%)'
Configuration: dbCNV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	90.6% Specificity (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source dbCNV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'dbCNV', column 'Specificity (%)'
Configuration: MarCNV-ISV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	69.8% Accuracy (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source MarCNV-ISV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'MarCNV-ISV', column 'Accuracy (%)'
Configuration: MarCNV-ISV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	43.9% F1 (ACMG five-category classification; averaging across classes not stated) percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source MarCNV-ISV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'MarCNV-ISV', column 'F1(%)'
Configuration: MarCNV-ISV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	9.56% False positive rate (ACMG five-category classification; averaging across classes not stated) percent · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source MarCNV-ISV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'MarCNV-ISV', column 'FPR*(%)'
Configuration: MarCNV-ISV (Zhang et al. 2026)	Protocol: ACMG five-category CNV pathogenicity classification against expert labels (Zhang et al. Table 3) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	0.55 MCC (ACMG five-category classification; averaging across classes not stated) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source MarCNV-ISV on the expert-classified ACMG CNV benchmark (five category) cnv-20261011-zhang2026-protocol-five-category Aggregation: Not reported Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Table 3, row 'MarCNV-ISV', column 'MCC*'

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7 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-11-02-a7f03c9e55a5
Property and statement	Original source and location	Review and provenance
Population 3,031 CNVs: 2,125 duplications (1,026 B, 22 LB, 886 VUS, 57 LP, 134 P) and 906 deletions (277 B, 13 LB, 329 VUS, 59 LP, 228 P); sources a Brazilian neurodevelopmental cohort, spontaneous-abortion studies, prenatal VUS follow-up, a 3,429-amniocentesis CNV-seq cohort, a paediatric epilepsy genome cohort and the authors' internal CNV-seq cases (December 2019 to November 2025) Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ Table 2; Methods 'Establishing a clinical CNV benchmark dataset'; Results 'Dataset overview'; Data availability Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.population` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Reuse restrictions Available from the corresponding author on reasonable request Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ Table 2; Methods 'Establishing a clinical CNV benchmark dataset'; Results 'Dataset overview'; Data availability Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.reuse_restrictions` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Source location Table 2; Methods 'Establishing a clinical CNV benchmark dataset'; Results 'Dataset overview'; Data availability Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ Table 2; Methods 'Establishing a clinical CNV benchmark dataset'; Results 'Dataset overview'; Data availability Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.source_locator` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Split No split; whole set scored Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ Table 2; Methods 'Establishing a clinical CNV benchmark dataset'; Results 'Dataset overview'; Data availability Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.split` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Version As assembled by Zhang et al. 2026 (not public) Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ Table 2; Methods 'Establishing a clinical CNV benchmark dataset'; Results 'Dataset overview'; Data availability Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.version` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Description CNVs with ACMG five-category classifications from five published clinical studies and an internal CNV-seq laboratory set, deduplicated and lifted to hg38. Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ Table 2; Methods 'Establishing a clinical CNV benchmark dataset'; Results 'Dataset overview'; Data availability Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `description` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Name Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs) Context-only references	Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) Original source ↗ Table 2; Methods 'Establishing a clinical CNV benchmark dataset'; Results 'Dataset overview'; Data availability Version: Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML Retrieved: 2026-10-11T21:36:59Z	not individually reviewed No individual claim review recorded Audit details Field: `name` Source artifact SHA-256: `a0810a8bff1ab3d2603a4832a56291cc182c8530aa8dffc6ac5318abd989ada0` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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Release 2026-11-02-a7f03c9e55a5 · Record review: source checked

1 source record and release history

Can automated copy number variation pathogenicity prediction tools replace manual review under the ACMG framework? A clinical validation (Zhang, Zhang and Chen, Global Medical Genetics 2026) · Original source · Global Medical Genetics 13:100096 (2026), published online 2026-03-06; PMC13018948 full-text XML

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Technical metadata and extraction receipts

Stable ID: cnv-20261011-zhang2026-data-expert-acmg-cnv-benchmark

areas: dna-genomes
contexts: clinical_research
version: As assembled by Zhang et al. 2026 (not public)
population: 3,031 CNVs: 2,125 duplications (1,026 B, 22 LB, 886 VUS, 57 LP, 134 P) and 906 deletions (277 B, 13 LB, 329 VUS, 59 LP, 228 P); sources a Brazilian neurodevelopmental cohort, spontaneous-abortion studies, prenatal VUS follow-up, a 3,429-amniocentesis CNV-seq cohort, a paediatric epilepsy genome cohort and the authors' internal CNV-seq cases (December 2019 to November 2025)
split: No split; whole set scored
reuse restrictions: Available from the corresponding author on reasonable request
source locator: Table 2; Methods 'Establishing a clinical CNV benchmark dataset'; Results 'Dataset overview'; Data availability

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