Protocol

Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL)

As for duplications, on the 906 deletions.

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5 recorded evaluations, 15 metric rows. A comparison chart has not yet been validated for these results. The table retains the individual findings and their sources.

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All evaluations

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

Protocol: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) · Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)

Sorted by Recall (per-class recall for reference class benign (benign and likely benign merged), three-category scheme; printed as 'Accuracy') (higher is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.

Tested configuration	Recall (per-class recall for reference class benign (benign and likely benign merged), three-category scheme; printed as 'Accuracy')	Recall (per-class recall for reference class pathogenic (pathogenic and likely pathogenic merged), three-category scheme; printed as 'Accuracy')	Recall (per-class recall for reference class variant of uncertain significance, three-category scheme; printed as 'Accuracy')
dbCNV (Zhang et al. 2026)Evaluation	84.1% (best in column)	97.2% (best in column)	19.8%
X-CNV (Zhang et al. 2026)Evaluation	81.7%	95.1%	1.22%
AnnotSV (Zhang et al. 2026)Evaluation	75.2%	95.2%	42.9%
MarCNV-ISV (Zhang et al. 2026)Evaluation	65.9%	71.8%	89.9%
ClassifyCNV (Zhang et al. 2026)Evaluation	53.1%	68.6%	95.7% (best in column)

All 15 result rows with coverage, uncertainty and sources

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Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

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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: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	75.2% Recall (per-class recall for reference class benign (benign and likely benign merged), three-category scheme; printed as 'Accuracy') 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 (three category deletions) cnv-20261011-zhang2026-protocol-three-category-deletions 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 4, block 'DEL', row 'B', column 'AnnotSV (%)'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	95.2% Recall (per-class recall for reference class pathogenic (pathogenic and likely pathogenic merged), three-category scheme; printed as 'Accuracy') 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 (three category deletions) cnv-20261011-zhang2026-protocol-three-category-deletions 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 4, block 'DEL', row 'P', column 'AnnotSV (%)'
Configuration: AnnotSV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	42.9% Recall (per-class recall for reference class variant of uncertain significance, three-category scheme; printed as 'Accuracy') 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 (three category deletions) cnv-20261011-zhang2026-protocol-three-category-deletions 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 4, block 'DEL', row 'VUS', column 'AnnotSV (%)'
Configuration: ClassifyCNV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	53.1% Recall (per-class recall for reference class benign (benign and likely benign merged), three-category scheme; printed as 'Accuracy') 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 (three category deletions) cnv-20261011-zhang2026-protocol-three-category-deletions 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 4, block 'DEL', row 'B', column 'ClassifyCNV (%)'
Configuration: ClassifyCNV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	68.6% Recall (per-class recall for reference class pathogenic (pathogenic and likely pathogenic merged), three-category scheme; printed as 'Accuracy') 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 (three category deletions) cnv-20261011-zhang2026-protocol-three-category-deletions 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 4, block 'DEL', row 'P', column 'ClassifyCNV (%)'
Configuration: ClassifyCNV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	95.7% Recall (per-class recall for reference class variant of uncertain significance, three-category scheme; printed as 'Accuracy') 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 (three category deletions) cnv-20261011-zhang2026-protocol-three-category-deletions 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 4, block 'DEL', row 'VUS', column 'ClassifyCNV (%)'
Configuration: dbCNV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	84.1% Recall (per-class recall for reference class benign (benign and likely benign merged), three-category scheme; printed as 'Accuracy') 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 (three category deletions) cnv-20261011-zhang2026-protocol-three-category-deletions 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 4, block 'DEL', row 'B', column 'dbCNV (%)'
Configuration: dbCNV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	97.2% Recall (per-class recall for reference class pathogenic (pathogenic and likely pathogenic merged), three-category scheme; printed as 'Accuracy') 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 (three category deletions) cnv-20261011-zhang2026-protocol-three-category-deletions 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 4, block 'DEL', row 'P', column 'dbCNV (%)'
Configuration: dbCNV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	19.8% Recall (per-class recall for reference class variant of uncertain significance, three-category scheme; printed as 'Accuracy') 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 (three category deletions) cnv-20261011-zhang2026-protocol-three-category-deletions 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 4, block 'DEL', row 'VUS', column 'dbCNV (%)'
Configuration: MarCNV-ISV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	65.9% Recall (per-class recall for reference class benign (benign and likely benign merged), three-category scheme; printed as 'Accuracy') 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 (three category deletions) cnv-20261011-zhang2026-protocol-three-category-deletions 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 4, block 'DEL', row 'B', column 'MarCNV-ISV (%)'
Configuration: MarCNV-ISV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	71.8% Recall (per-class recall for reference class pathogenic (pathogenic and likely pathogenic merged), three-category scheme; printed as 'Accuracy') 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 (three category deletions) cnv-20261011-zhang2026-protocol-three-category-deletions 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 4, block 'DEL', row 'P', column 'MarCNV-ISV (%)'
Configuration: MarCNV-ISV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	89.9% Recall (per-class recall for reference class variant of uncertain significance, three-category scheme; printed as 'Accuracy') 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 (three category deletions) cnv-20261011-zhang2026-protocol-three-category-deletions 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 4, block 'DEL', row 'VUS', column 'MarCNV-ISV (%)'
Configuration: X-CNV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	81.7% Recall (per-class recall for reference class benign (benign and likely benign merged), three-category scheme; printed as 'Accuracy') percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source X-CNV on the expert-classified ACMG CNV benchmark (three category deletions) cnv-20261011-zhang2026-protocol-three-category-deletions 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 4, block 'DEL', row 'B', column 'X-CNV (%)'
Configuration: X-CNV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	95.1% Recall (per-class recall for reference class pathogenic (pathogenic and likely pathogenic merged), three-category scheme; printed as 'Accuracy') percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source X-CNV on the expert-classified ACMG CNV benchmark (three category deletions) cnv-20261011-zhang2026-protocol-three-category-deletions 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 4, block 'DEL', row 'P', column 'X-CNV (%)'
Configuration: X-CNV (Zhang et al. 2026)	Protocol: Three-category CNV classification, per-class agreement on deletions (Zhang et al. Table 4, DEL) Dataset: Expert-classified ACMG CNV benchmark (Zhang et al. 2026, 3,031 CNVs)	1.22% Recall (per-class recall for reference class variant of uncertain significance, three-category scheme; printed as 'Accuracy') percent · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source X-CNV on the expert-classified ACMG CNV benchmark (three category deletions) cnv-20261011-zhang2026-protocol-three-category-deletions 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 4, block 'DEL', row 'VUS', column 'X-CNV (%)'

Source checking is not independent reproduction. Release 2026-11-02-a7f03c9e55a5.

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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-protocol-three-category-deletions

areas: dna-genomes
contexts: clinical_research
protocol: As for duplications, on the 906 deletions.
version: Table 4 rows 'DEL' B, VUS, P
source locator: Table 4 rows 'DEL' B, VUS, P; Results 'Analysis of three-category performance'
limitations: Reference labels are expert ACMG classifications, a semi-quantitative standard with known inter-reviewer variability (Discussion paragraph 5).; The set is enriched for pregnancy-loss and prenatal cases and is imbalanced (benign is about half of the duplications), which the authors say affects the metrics (Discussion paragraphs 5 and 6).; Classification of already detected CNVs from their coordinates; no read data or detection step is evaluated.; Some training sets of the evaluated tools (ClinVar, ClinGen, DGV) may overlap the published cohorts used as reference; the paper does not test this.; Two of the three authors (X. Zhang and D. Chen) are authors of dbCNV (reference 24), developed in the same laboratory, so the dbCNV rows are author_reported. dbCNV's original test set was that laboratory's internal dataset, and this benchmark includes the laboratory's internal CNV-seq cases (December 2019 to November 2025); overlap is not tested. The paper declares no competing interests.

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